Computational Intelligence E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book provides a comprehensive exploration of computational intelligence techniques and their applications, offering valuable insights into advanced information processing, machine learning concepts, and their impact on agile manufacturing systems.
Computational Intelligence presents a new concept for advanced information processing. Computational Intelligence (CI) is the principle, architecture, implementation, and growth of machine learning concepts that are physiologically and semantically inspired. Computational Intelligence methods aim to develop an approach to evaluating and creating flexible processing of human information, such as sensing, understanding, learning, recognizing, and thinking. The Artificial Neural Network simulates the human nervous system’s physiological characteristics and has been implemented numerically for non-linear mapping. Fuzzy Logic Systems simulate the human brain’s psychological characteristics and have been used for linguistic translation through membership functions and bioinformatics. The Genetic Algorithm simulates computer evolution and has been applied to solve problems with optimization algorithms for improvements in diagnostic and treatment technologies for various diseases. To expand the agility and learning capacity of manufacturing systems, these methods play essential roles. This book will express the computer vision techniques that make manufacturing systems more flexible, efficient, robust, adaptive, and productive by examining many applications and research into computational intelligence techniques concerning the main problems in design, making plans, and manufacturing goods in agile manufacturing systems.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Introduction
1 Computational Intelligence Theory: An Orientation Technique
1.1 Computational Intelligence
1.2 Application Fields for Computational Intelligence
1.3 Computational Intelligence Paradigms
1.4 Architecture Assortment
1.5 Myths About Computational Intelligence
1.6 Supervised Learning in Computational Intelligence
1.7 Training Set Manipulation
1.8 Conclusion
References
2 Nature-Inspired Algorithms for Computational Intelligence Theory—A State-of-the-Art Review
2.1 Introduction
2.2 Related Works
2.3 Optimization and Its Algorithms
2.4 Metaheuristic Optimization Methods
2.5 Computational and Autonomous Systems
2.6 Unresolved Issues for Continued Study
References
3 AI-Based Computational Intelligence Theory
3.1 Computational Intelligence
3.2 Designing Expert Systems
3.3 Core of Computational Intelligence
3.4 Research and Development
3.5 New Opportunities and Challenges
3.6 Applications
3.7 Case Study: YOLO v7 for Object Detection in TensorFlow
3.8 Results
3.9 Performance Analysis
3.10 Challenges in Automation
3.11 Conclusion
References
4 Information Processing, Learning, and Its Artificial Intelligence
4.1 Introduction—Artificial Intelligence
4.2 Artificial Intelligence and Its Learning
4.3 Artificial Intelligence’s Effects on IT
4.4 Examples of Artificial Intelligence
4.5 Data Processing and AI in Human-Centered Manufacturing
4.6 Information Learning
4.7 Results
4.8 Conclusion
References
5 Computational Intelligence Approach for Exploration of Spatial Co-Location Patterns
5.1 Introduction
5.2 Spatial Data Mining
5.3 Preliminaries
5.4 Proposed Grid-Conditional Neighborhood Algorithm
5.5 Experimental Setup and Analysis
5.6 Discussion and Conclusion
References
6 Computational Intelligence-Based Optimal Feature Selection Techniques for Detecting Plant Diseases
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Framework
6.4 Simulation Results
6.5 Summary
References
7 Protein Structure Prediction Using Convolutional Neural Networks Augmented with Cellular Automata
7.1 Introduction
7.2 Methods
7.3 Design of the Model
7.4 Results and Comparisons
7.5 Conclusion
References
8 Modelling and Approximating Renewable Energy Systems Using Computational Intelligence
8.1 Introduction
8.2 Expert System
8.3 Artificial Neural Networks
8.4 ANN in Renewable Energy Systems
8.5 Conclusion
References
9 Computational Intelligence and Deep Learning in Health Informatics: An Introductory Perspective
9.1 Introduction
9.2 Mobile Application in Health Informatics Using Deep Learning
9.3 Health Informatics Wearables Using Deep Learning
9.4 Electroencephalogram
9.5 Conclusion
References
10 Computational Intelligence for Human Activity Recognition (HAR)
10.1 Introduction
10.2 Fuzzy Logic in Human Judgment and Decision-Making
10.3 Artificial Neural Networks: From Perceptrons to Modern Applications
10.4 Swarm Intelligence
10.5 Evolutionary Computing
10.6 Artificial Immune System
10.7 Conclusion
References
11 Computational Intelligence for Multimodal Analysis of High-Dimensional Image Processing in Clinical Settings
11.1 Basics of Machine Learning
11.2 Feature Extraction
11.3 Selection of Features
11.4 Statistical Classifiers
11.5 Neural Networks
11.6 Biometric Analysis
11.7 Data from High-Resolution Medical Imaging
11.8 Computational Architectures
11.9 Timing and Uncertainty
11.10 AI and Risk of Harm
11.11 Conclusion
References
12 A Review of Computational Intelligence-Based Biometric Recognition Methods
12.1 Introduction
12.2 Computational Intelligence
12.3 CI-Based Biometric Recognition
12.4 Applications
12.5 Conclusion
References
13 Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging
13.1 Introduction
13.2 Hyperspectral Imaging (HSI)
13.3 State-of-the-Art Techniques for BC Detection
13.4 Artificial Intelligence in BC Detection Using HSI
13.5 Discussion and Conclusion
References
14 Shedding Light into the Dark: Early Oral Cancer Detection Using Hyperspectral Imaging
14.1 Introduction
14.2 HSI in HNC Detection
14.3 Deep Learning in
In Vivo
HSI
14.4 Conclusion and Future Research Directions
References
15 Machine Learning Techniques for Glaucoma Screening Using Optic Disc Detection
15.1 Introduction
15.2 Glaucoma Screening with Optic Disc and Classification
15.3 Experimental Section
15.4 Conclusion
References
16 Role of Artificial Intelligence in Marketing
16.1 Introduction
16.2 New Trends of AI in Marketing
16.3 Aspects of AI in Marketing across Different Industries
16.4 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Comparison of gradient-based and gradient-free algorithms.
Table 2.2 Comparing metaheuristics and legacy methods’ key features and charac...
Table 2.3 Nature-inspired algorithmic components vs. self-organization systems...
Chapter 3
Table 3.1 Comparison table of object detection methods.
Chapter 5
Table 5.1 Different crime categories in dataset.
Table 5.2 Attribute type in dataset.
Table 5.3 Derived spatial pattern for 1 MB data.
Chapter 6
Table 6.1 The proposed CSFE framework’s classification accuracy.
Chapter 7
Table 7.1 Comparison of the performance with existing protein structure predic...
Table 7.2 Comparison of performance with existing protein structure prediction...
Table 7.3 Secondary, ternary, and quaternary protein prediction comparisons (a...
Chapter 9
Table 9.1 Health info-based mobile apps using deep learning algorithms.
Table 9.2 Wearable using deep learning algorithms.
Table 9.3 Electroencephalography using deep learning algorithms.
Chapter 12
Table 12.1 Overview of biometric steps of based on computational intelligence ...
Chapter 13
Table 13.1 Types and stages of breast cancer.
Chapter 14
Table 14.1 Different stages of head and neck cancer.
Table 14.2 Biological differences between cancer tissue and normal tissue.
Table 14.3
Ex vivo
head and neck cancer.
Table 14.4
In vivo
head and neck cancer.
Chapter 15
Table 15.1 Test cases for input processes.
Table 15.2 Performance results of segmentation methods for the RIM dataset.
Table 15.3 Performance results of segmentation methods for the DRION dataset.
List of Illustrations
Chapter 1
Figure 1.1 Computational intelligence paradigms.
Figure 1.2 A biological neuron.
Figure 1.3 An artificial neuron.
Figure 1.4 Structure of ANN.
Figure 1.5 Illustration of overfitting.
Chapter 2
Figure 2.1 Optimization process of an ant colony.
Figure 2.2 Flower pollination process.
Figure 2.3 Evolutionary algorithm steps.
Figure 2.4 Echo location behavior of BAT.
Figure 2.5 Cuckoo’s nest.
Figure 2.6 Novel method for firefly algorithm (attracts by light).
Figure 2.7 Particle swarm optimization.
Chapter 3
Figure 3.1 Working of explainable AI.
Figure 3.2 Waymo—autonomous car.
Figure 3.3 Overall working of ChatGPT.
Figure 3.4 Atlas.
Figure 3.5 (a) COTS detected at coral reefs. (b) COTS identified in blur image...
Figure 3.6 Performance analysis.
Chapter 4
Figure 4.1 Intelligent tutorial system.
Figure 4.2 AI in human-centered manufacturing.
Figure 4.3 Virtual reality (VR).
Figure 4.4 Artificial intelligence invoice process.
Chapter 5
Figure 5.1 Spatial dataset.
Figure 5.2 PR and PI values with respect to co-location patterns.
Figure 5.3 MapReduce execution model.
Figure 5.4 Block diagram of the proposed system.
Figure 5.5 Flow diagram of proposed system.
Figure 5.6 Search neighbor algorithm.
Figure 5.7 Group neighbor algorithm.
Figure 5.8 Pattern search algorithm.
Figure 5.9 Top-K algorithm.
Figure 5.10 Execution time for different data sizes.
Figure 5.11 Spatial pattern count for different data sizes.
Chapter 6
Figure 6.1 Proposed framework.
Figure 6.2 Process flow at monitoring site.
Figure 6.3 Pomegranate plant (a) Alternaria Alternata, (b) Cercospora leaf spo...
Figure 6.4 Anthracnose image after HSV transformation: (a) V component image, ...
Figure 6.5 Anthracnose segmented image: (a) V component image, (b) H component...
Figure 6.6
Alternaria alternata
image after HSV transformation: (a) V componen...
Figure 6.7 HSV transformed image of Cercospora leaf spot. (a) V component imag...
Figure 6.8 Cercospora leaf spot segmented image (a) V component image, (b) H c...
Figure 6.9 Bar chart of the accuracy rate for infectious illnesses of pomegran...
Chapter 7
Figure 7.1 Working of cellular automata on a rule <252,18,54>.
Figure 7.2 Design of CNN-CA-P.
Figure 7.3 Training vs. validation accuracy.
Figure 7.4 Training vs. validation loss.
Figure 7.5 PseAAC, CATH accuracy comparisons.
Figure 7.6 Precision, recall, F1 score, and AUC accuracy comparisons.
Figure 7.7 Secondary, ternary, and quaternary protein prediction comparisons.
Chapter 8
Figure 8.1 Feed-forward multilayer neural network.
Chapter 9
Figure 9.1 System structure of deep learning-based wearable and mobile apps.
Figure 9.2 System structure of classification of EEG using deep learning.
Chapter 10
Figure 10.1 HAR categories.
Figure 10.2 Five paradigms of computational intelligence.
Figure 10.3 Basic fuzzy logic architecture.
Figure 10.4 Simple ANN.
Figure 10.5 Feed forward.
Figure 10.6 Feed backward.
Figure 10.7 Graphical illustration of a basic swarm intelligence.
Figure 10.8 Graphical illustration of basic AIS.
Chapter 11
Figure 11.1 Process of classification.
Figure 11.2 Neural network with multilayer.
Figure 11.3 Identification, categorization, and detection.
Figure 11.4 Biometric identification.
Figure 11.5 Biometric categorization.
Figure 11.6 Biometric detection.
Figure 11.7 AIA Proposal risk-based.
Figure 11.8 Biometric techniques.
Figure 11.9 Cloud-based tools such as MD.
Figure 11.10 Machine learning models trained with standard methods.
Figure 11.11 Account for classification uncertainty.
Chapter 12
Figure 12.1 Computational intelligence categories.
Figure 12.2 An overview of biometric recognition system.
Figure 12.3 Military biometric market revenue in the year of 2019.
Chapter 13
Figure 13.1 Comparison of multispectral and HSI. In this example, a multispect...
Figure 13.2 Actual laboratory HSI setup.
Figure 13.3 (a) Reflection imaging and (b) transmission imaging.
Figure 13.4 HSI
ex vivo
image processing techniques.
Figure 13.5 Typical
in vivo
examinations.
Figure 13.6
In vivo
image analysis.
Figure 13.7 Hybrid spectra net.
Chapter 14
Figure 14.1 HSI cube.
Figure 14.2 Two diagnosis and therapeutic windows of the spectrum.
Figure 14.3 Absorption coefficient of biological tissues at different waveleng...
Figure 14.4 HSI imaging methods.
Figure 14.5 HSI laboratory imaging setup.
Figure 14.6 AI techniques usage in HNC research.
Figure 14.7 CNN architecture for HNC.
Figure 14.8 Deep learning models for the HNC using HSI.
Figure 14.9 Future research directions.
Chapter 15
Figure 15.1 Flow diagram of the glaucoma screening.
Figure 15.2 ROI extraction.
Figure 15.3 Optic cup region from median filtering.
Figure 15.4 Optic cup region after blood vessel removal.
Figure 15.5 Experimental images.
Figure 15.6 Performance analysis of segmentation methods using the RIM and DRI...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Introduction
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Computational Intelligence
Theory and Applications
Edited by
T. Ananth Kumar
E. Golden Julie
Venkata Raghuveer Burugadda
Abhishek Kumar
and
Puneet Kumar
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-21422-8
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
Introduction
Chapter 1 encompasses computing paradigms inspired by nature and cognition, rooted in evolution, fuzzy systems, and neural networks. Computational intelligence (CI) underpins potent AI systems, notably deep learning, a cornerstone of modern AI technology. It is the bedrock of highly effective AI systems, driving advancements like video games and cognitive development.
Chapter 2 handles merging evolutionary computation, neural networks, and fuzzy systems decades ago. Nature-inspired algorithms have evolved, proving more adaptable for optimization. Various types of these algorithms, particularly those centered on self-organizing natural communities, are actively developed. This article compares state-of-the-art optimization techniques with established gradient-based and gradient-free methods. It also identifies unresolved issues in optimization and meta-heuristics for future research.
Chapter 3 demotes AI’s significance in modern industries for its robust computing capabilities, which process extensive data, yielding valuable insights and aiding decision-making. AI customizes experiences through user data analysis. Computational intelligence research benefits individuals and society, enhancing data processing for intelligent AI systems. This chapter explores various dimensions of computational intelligence, driving societal development and economic progress. It delves into hybrid models, ensemble techniques, and practical AI applications, offering insights for future researchers and scholars in advancing computational intelligence.
Chapter 4 delves into an AI system based on cognitive mechanisms for visual data processing. It explores the relationship between this model and cognitive processes, focusing on selective attention. AI is viewed as a transformative force in human–machine interaction, impacting organizational dynamics, communication, and ecosystems. While AI research can be complex, the information systems field is pivotal. The paper suggests that AI technology may surpass human instructors within a decade.
Chapter 5 deals with groups of spatial co-location patterns. This work introduces a computational intelligence method employing a grid clustered technique, enhancing pattern detection. The Top-K co-location technique is used for generating highly co-located spatial patterns. Implemented on the MapReduce framework, it accelerates the processing of large spatial datasets, improving efficiency. Experimental results confirm the algorithm’s effectiveness across various data sizes.
Chapter 6 introduces an efficient method for early detection and classification of plant illnesses using machine learning. Early detection of plant diseases is crucial in agriculture for increased profitability and yield protection. The approach involves capturing leaf images with a camera sensor, extracting essential features through segmentation, and utilizing SVM for disease categorization. The system’s effectiveness is evaluated for both detection and classification.
Chapter 7 predicts that protein structure, a deep learning challenge, has seen notable progress, yet room for improvement remains. Deriving tertiary, secondary, and quaternary structures from the primary is complex. Convolutional neural networks (CNNs) model interactions using features like amino acid sequences. Employing data from a protein data bank, we developed CNN-CA-P, augmenting cellular automata. Achieving high accuracy (96.56% secondary, 91.2% tertiary, and 86.32% quaternary), it outperforms baseline methods, evaluated on parameters like accuracy, AUC, precision, F1 score, and recall.
Chapter 8 explores the application of artificial intelligence techniques, specifically artificial neural networks (ANNs) and expert systems (ES), in modeling and forecasting renewable energy effectiveness. It presents various problems in renewable energy engineering to showcase how these systems operate. The research demonstrates the potential of AI as a design tool across different aspects of renewable energy engineering, affirming the efficacy of neural networks in this domain.
Chapter 9 presents health informatics and a surge in data analytics driven by diverse multimodal data. This has sparked interest in tailored machine learning (ML) models. Deep learning (DL), rooted in neural networks, has emerged as a potent tool in AI, promising transformative impacts. Its capacity for complex feature enhancement and semantic analysis, coupled with computational advancements, fuels its prominence. This chapter comprehensively assesses DL’s advantages, potential limitations, and prospects, particularly in health-related contexts. The investigation focuses on critical applications, spanning bioinformatics, continuous sensing, medical imaging, and public health.
Chapter 10 tackles real-world issues using nature-inspired methods, distinct from formal models. CI plays a crucial role in human activity recognition (HAR), capturing activities via sensors and processing them. HAR holds vital information on identity, personality, gestures, and more, impacting interpersonal interactions. This chapter delves into CI paradigms for HAR, like fuzzy logic, artificial neural networks, swarm intelligence, evolutionary computing, and artificial immune systems. Researchers are advancing HAR with CI algorithms, showcasing CI’s versatility and discussing its benefits and drawbacks in various research contexts.
Chapter 11 deals with AI with healthcare. Biomedical image processing employs image analysis, machine learning, and cloud technology. Fuzzy logic, Bayesian inference, and statistics aid medical disease detection. Overcoming challenges of high dimensionality, class imbalance, and limited databases, modern technology provides superior results. Cloud computing enables global accessibility for data storage and processing, improving diagnostic accuracy for connected diseases. AI strives for precise, comprehensive solutions in biomedical processing.
Chapter 12 deals with computational intelligence (CI) methods, including sample augmentation, feature extraction, categorization, indexing, fusion, normalization, and anti-spoofing, which are crucial in creating biometric identities and addressing dataset challenges. CI enables complex nonlinear calculations and model development from training data, employing supervised and unsupervised training. This chapter explores CI-based biometric recognition methods.
Chapter 13 deals with hyperspectral imaging (HSI), which has gained prominence, especially in biomedical fields like cancer detection. Breast cancer (BC) is a significant global health concern, with over 1.3 million cases in India. Early detection improves survival rates. Various optical techniques are employed, each with its advantages and drawbacks. Biopsies, the current validation method, are invasive. Non-invasive methods like HSI show promise. This chapter comprehensively reviews HSI for breast cancer detection, covering advanced deep-learning frameworks for automated diagnosis.
Chapter 14 also deals with healthcare and AI. Oral cancer (OC) is particularly prevalent in India, accounting for a significant percentage of cases and deaths. Early detection is crucial for survival. Various imaging techniques exist, with drawbacks. Emerging methods like hyperspectral imaging (HSI) show promise for non-invasive, safe, and precise diagnosis. HSI combined with deep learning techniques like CNNs and 3DCNNs holds potential for early OC detection. This chapter outlines these advancements and suggests future research directions.
Chapter 15 deals with image processing, which plays a crucial role in human eye recognition. Prolonged computer use can lead to visual problems. Optic disc (OD) is vital for diagnosing retinal diseases. It is characterized by high fractal dimensions due to blood vessels. OD’s location helps diagnose conditions like glaucoma. This screening system aids in glaucoma detection through OD segmentation. Glaucoma is a chronic eye disease causing irreversible vision loss. Retinal image features like OD, optic cup (OC), and neuro retinal rim (NRR) are crucial for disease identification. This work aims to enhance OD detection using multiple segmentation algorithms. The method involves directional matched filtering, vessel detection, and cup boundary assessment for OC segmentation. Machine learning algorithms further aid in glaucoma diagnosis. This method shows promising potential with a solid correlation to ground truth segmentation results.
Chapter 16 revolutionized AI in marketing, providing innovative ways to engage customers. It processes vast data, offering insights for tailored marketing strategies. Social media and placement automation streamlines operations, saving time and enhancing plans. AI-powered chatbots handle basic inquiries, freeing marketers. Predictive analytics use client data to forecast behavior and refine products and services. Embracing AI ensures a competitive edge in the digital landscape, enabling customized, effective marketing tactics. This chapter explores AI’s role in marketing, presenting opportunities and challenges. It is a valuable resource for marketing professionals, educators, and students keen on understanding AI’s impact in the field.
1Computational Intelligence Theory: An Orientation Technique
S. Jaisiva1*, C. Kumar2, S. Sakthiya Ram3, C. Sakthi Gokul Rajan1 and P. Praveen Kumar4
1Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
2Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
3Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India
4Department of Information Technology, Sri Manakula Vinayagar Engineering College, Pondicherry, India
Abstract
The ability of a system to change its behavior to reach its objective in a variety of settings is intelligence. In reality, a different definition of computational intelligence (CI) is that it entails real-world adaption in challenging and shifting situations. In other words, it serves as a precise illustration of a notion. Adaptation and computational intelligence are intimately linked concepts. The concept, design, implementation, and advancement of computing paradigms driven by natural and cognitive motivations is known as CI. Evolutionary computation, fuzzy systems, and neural networks have historically been the three major foundations of CI. However, over time, various computing models that were inspired by nature have emerged. Sustainable smart information system, such as the creation of video games and cognitive developmental systems, heavily relies on CI. Deep learning study, especially that on deep convolutional neural networks, has exploded in recent years. Deep learning is currently the main approach for artificial intelligence. Deep learning has become the main technology for AI. In reality, CI is the foundation of some of the most effective AI systems.
Keywords: Computational intelligence, artificial intelligence, biological intelligence, neural networks, fuzzy systems, optimization, evolutionary computation
1.1 Computational Intelligence
Intelligence is a trait shared by all decision-makers with a goal. An analysis paradigm known as an artificial neural network (ANN) is loosely framed on the basis of the human brain massively parallel architecture [1]. It replicates a massively parallel, linked computing framework with a large number of very straightforward individual processing components (PEs). The phrases artificial neural network and neural network will now be used equally throughout this chapter. Fuzzies are non-statistical inexactitude and ambiguity in info, as used in this article. The majority of notions used or expressed in the real world are hazy. For instance, the sentence “It’s somewhat misty outdoors right now” combines the notions of being pretty and, even before, a long period of time. (One may even contend that the term is ambiguous and inaccurate enough to be hazy.) Fuzzy sets simulate the characteristics of estimation, ambiguity, and inaccuracy. Fuzzy membership values in a fuzzy set represent the membership dimensions (or grades) of the set’s components. It will be demonstrated that the fundamental concept of fuzzy set theory is a membership function, which is the same as a fuzzy set [2].
Crossover, mutations, and the survival of the fittest are examples of natural evolutionary phenomena that are incorporated into genetic algorithms, which are search algorithms. They are utilized for categorization as well as optimization more frequently. While genetic algorithms incorporate crossover, evolutionary programming approaches do not. Instead, they depend on mutation and the survival of the fittest. Comparable to genetic algorithms, evolution tactics frequently employ a distinct kind of mutation in addition to using combination to share data across members of population rather than crossover [3]. Computer programs can evolve using a technique called genetic programming. Hierarchical tree topologies are frequently used to manipulate structures. Potential solutions are dispersed throughout the problem space by particles in particle swarm optimization. The issue space’s chosen locations where prior fitness values have been high are where the particles are pushed. The term “computational intelligence” refers to a computing-based methodology that gives a system the capability to gain knowledge of novel situations, giving the system the appearance of possessing one or more rational qualities including generalization, discovery, connection, and abstraction. They are frequently made to resemble one or more characteristics of natural intelligence. In the illustration of a neural network paradigm is back-propagation, which presupposes a particular set of characteristics, such as the design and the learning algorithm [4]. A certain collection of options for each characteristic constitutes a paradigm. Introducing a separate paradigm includes putting together a group of characteristics that describe the desired behavior of the CI tool.
There are some words that should only be used with care. One such instance is neural networks, where it is important to be clear if we are taking about analytical tools for artificial neural network wetware. Let us explore the conceptual and technological underpinnings of computational intelligence tools and component approaches after providing the fundamental definitions [5]. We utilize and mention the caveat mentioned before. The creation of algorithmic models to address ever-more-complex issues is a key focus of algorithmic innovation. These clever algorithms are a subset of artificial intelligence, along with deductive reasoning, expert systems, case-based reasoning, and symbolic machine learning systems (AI). AI can be seen as a synthesis of various scientific areas, such as computer science, physiology, philosophy, sociology, and biology, just by looking at the broad range of AI methodologies [6].
Yet what exactly is intelligence? Definitions of intelligence continue to spark heated discussion. Dictionary definitions of intelligence include the capability for cognition and reason, as well as the capacity to perceive, comprehend, and benefit from experience (especially to a high degree). Innovation, ability, awareness, empathy, and instinct are other terms used to characterize characteristics of intelligence.
Can computers think for themselves? Even now, there is more disagreement over this issue than over how to define intelligence. Alan Turing gave this issue a lot of study in the middle of the 20th century. He thought it was possible to build devices that could duplicate the functions of the human brain. Turing firmly felt that a well-designed computer could perform every task that the brain was capable of. His predictions are still prophetic more than fifty years later. Smaller biological neural system components have been successfully modeled, but the complicated task of modeling is an essential component of mankind intelligence and remains unsolved [7].
The Turing test, created by Turing in 1950, is a measurement of computing intelligence. The test involved asking questions of both a person and a machine using a keyboard. The computer might be thought to be smart if the interviewer was unable to tell the computer from the person. Turing anticipated that by the year 2000, a system will be able to compete with the testing and training of 70% chance. Has his conviction been realized? In order to avoid jumping into yet another argument, the reader is left to choose the solution to this issue. However, the information in this book may help to clarify some aspects of the response [8].
The IEEE Neural Networks Council of 1996 gave a more modern version of artificial intelligence as the research of how to get computers to perform tasks that people are good at. These processes include the AI paradigms that can generalize, synthesize, discover, and connect as well as learn novel contexts. While specific approaches and techniques from various CI paradigms have been effectively used to address issues in the real world, the current trend is to create hybridization of models because no one model is always better than the others. By doing this, we strengthen the areas where each component of the hybrid CI system excels and do away with those where it falls short. Swarm intelligence is a category of the CI concepts, despite the fact that many investigators believe they should only fall within the category of synthetic biology [9].
1.2 Application Fields for Computational Intelligence
There are applications for which every computational intelligence element technique is particularly well suited. A particular problem might be solvable by either a neural network or a fuzzy system, but at varying standards of achievement; therefore, consider the fact that main applications may intersect. It might not even be typical of all the important application fields. It is intended to give some insight into the variety of issues that have been addressed by using CI’s component techniques.
1.2.1 Neural Networks
Generally speaking, neural networks are best suited for five types of applications. The first three have a connection.
1.2.1.1 Classification [10]
This section examines which of a number of predefined classes most accurately captures an input sequence. Usually, there are not many classes compared to the quantity of inputs. One illustration determines whether a specific EEG data section represents an epileptiform spike waveform. Another type of clustering is the creation of nonlinear mappings between high-dimensional spaces by neural networks. This application area includes several forms of video image processing (such as tumor diagnosis).
1.2.1.2 Clustering or Compression
Although categorization is a part of this field, compression algorithm can also be used to describe it. Think of natural language processing as an example of how the complexity of a source is considerably decreased. Lowering the number of bits necessary to represent a data block within a specific allowed error range is another. In other words, less bytes than in the source information can be used to reproduce the original block of data within the allowed mistake.
1.2.1.3 Generation of Sequences or Patterns
In contrast to the first three, this fourth area does not entail any classification. Using examples as training data, a network creates these patterns [11]. At an instant, to duplicate a particular kind of harmonious progression, the network may be able to create “original” renditions of that style of music. Another option is to train a neural network to emulate or model anything. There may not be any “correct” solutions because the system being replicated has inherent unpredictability, yet the system can perhaps be quantitatively defined. These statistical characteristics can then be incorporated into the network simulation.
1.2.1.4 Control Systems
Among the quickest-evolving application areas for neural networks is control systems. It is being used extensively for a number of reasons. An ANN-based control system can first handle all sets of nonlinear effects. (An approximate linearity of the system is not required.) Second, when building the control system, the chaotic system can be modeled using a network. Third, compared to other, more conventional methods, developing a neural network control system often takes a lot less time. For each of the five uses, there appear to be more and more emerging every day. Some implementations are unique to a field of study [12]. EEG waveform classification and appendicitis diagnosis are two examples of fields such as medicine. Neural networks are used in accounting and commerce to process loan applications from financing companies and to trade options on commodity futures contracts. Neural networks are able to govern the locations of several cars on an interstate at once in the automotive sector.
1.2.1.5 Evolutionary Computation
Optimization and categorization are the two basic applications of evolutionary algorithms. Since optimization is the subject of the majority of engineering disciplines for evolutionary computation, optimization is the main topic of discussion in this theme.
1.2.2 Fuzzy Logic
Numerous engineering fields, including robotics and control, modeling, and geotechnical sciences, use fuzzy logic in a variety of applications. Medicine, management, decision analysis, and computer science are further application fields. Similar to neural networks, new applications pop up practically every day. Fuzzy expert networks and adaptive logic are two of the key application domains [13].
1.2.2.1 Fuzzy Control Systems
Fuzzy control systems have been used in traffic signal circuits, household appliances, video cameras, metro systems, cement kilns, and a number of automotive subsystems, along with the gearbox and emergency systems. The circuitry within a video camera that stabilizes the image despite the user’s shaky hand placement is one technology that many people are familiar with. Fuzzy expert systems have been used in a variety of fields, including corporate strategy selection, industrial automation, medical diagnosis, planning, and currency trading.
1.2.2.2 Fuzzy Systems
According to conventional set theory, a component may either be a member of a set or it cannot. Similar restrictions apply to the results of an implicit learning procedure in binary-valued logic, which calls for model parameters to be either 0 or 1. Unfortunately, human logic is rarely this precise. Normally, there is a level of ambiguity in both our perceptions and thinking [14].
Probabilistic thinking is made possible by fuzzy sets and fuzzy logic. When using fuzzy sets, an element can be quite assured that it conforms to a range. Fuzzy logic enables inference of actual revelations from these ambiguous facts, each of which has a degree of certainty attached to it. In a way, rational thinking can be modeled using fuzzy sets and reasoning. Fuzzy systems have been effectively used to regulate traffic lights, lifts, cog shifting and brake mechanisms in automobiles, and many other systems.
1.2.2.3 Behavioral Motivations for Fuzzy Logic
Fuzzy systems are lacking a physiological rationale or foundation at the subcellular and cellular levels. It manifests itself in the way the creature behaves, or in how the creature engages with its surroundings. The methodologies before have a strong biological foundation, but fuzzy logic mostly works with uncertainty and vagueness. We do not exist in a universe of truth and untruth, ones and zeros, black and white, or other objective facts. Our emotions, interactions, and perceptions almost always contain a significant amount of unpredictability [15].
There are two primary categories of ambiguity. One is quantitative and is based on the probabilistic laws. The other kind of uncertainty is nonstatistical and relies on ambiguity, inaccuracy, or both. Fuzziness is a term used to describe nonprobability unpredictability. Fuzzy logic’s capacity to effectively collect and manage these hazy, disorganized thoughts is one of its key characteristics.
A system’s essential characteristic is fuzziness. By inspection or measurement, it is neither changed nor resolved. The representation of a complicated system can be made more tractable to analysis by accounting for some unpredictability. Thus, fuzzy logic offers a structure for the definition, description, and analysis of descriptive unpredictability. According to him, fuzziness results from a verbal lack of precision [16].
1.3 Computational Intelligence Paradigms
Artificial neural networks (NN), evolutionary computation (EC), swarm intelligence (SI), artificial immune systems (AIS), and fuzzy systems are the five basic paradigms that computational intelligence (CI) takes into account (FS). Figure 1.1 illustrates how deterministic methods are typically combined with CI approaches in addition to CI paradigms. The arrows show how different paradigm approaches can be merged to create architectures. Every CI paradigm has biochemical pathways at its foundation.
1.3.1 Artificial Neural Networks
A sophisticated, chaotic, and simultaneous computer is the nervous system. Even if events happen in the range of nanoseconds for semiconductor gates and milliseconds for brain systems, it can accomplish tasks like analytical thinking, vision, and motor control much more quickly than any computer. Investigation into algorithmic modeling of biological brain systems, also known as artificial neural networks, was stimulated by these traits as well as others like the capacity to learn, memorize, and still generalize (NN) [17].
Figure 1.1 Computational intelligence paradigms.
The cerebral cortex is thought to contain 60 trillion interconnections and between 10 and 500 billion axons. Each of the 1000 primary modules that make up the arrangement of the neurons has about 500 neural networks. The most successful artificial neural networks (NNs) used in neural modeling today are tiny, task-specific NNs. As long as you are limited by the limits of current processing capacity and memory size, challenges are handled rather feasible with reasonable NNs. Conversely, the intellect has the capacity to solve many issues at once by utilizing different brain regions.
Neurons, often known as nerve cells, are the fundamental components of biological brain networks. A neuron comprises of an axon, dendrites, and the cell body as represented in Figure 1.2. Neurons are incredibly linked, with connections often occurring in between axon and dendrite of two different neurons. The term “synapse” refers to this link. From the synapses, signals travel to the axon and cell body, from where they spread to all associated filaments. When a nerve cell fires, a signal travels to the axon of the unit. A pulse can be either excited or inhibited by a cell. The following Figure 1.3 shows the representation of an artificial neuron, which comprises of three different layers namely as input layer, middle layer and an output layer.
Figure 1.2 A biological neuron.
Figure 1.3 An artificial neuron.
Stacked networks of neural networks make up an artificial neural network (ANN). All the feasible ones are response linkages to earlier layers. Figure 1.4 shows an illustration of a typical NN construction.
Figure 1.4 Structure of ANN.
There have been numerous distinct NN types produced:
The Hopfield system is an example of a single-layered NN.
Multilayer feedforward NNs, such as conventional backpropagation.
Functioning link and number of units networks are examples of deep neural NNs.
Controlled and uncontrolled NNs are integrated to each other with similar operations.
These NN types have been applied to a variety of tasks, such as diagnostic techniques, natural language processing, data analysis, music composition, computer graphics, face recognition, strategy development, and decompression, among others.
1.3.2 Evolutionary Computation (EC)
The goal of EC is to replicate ordinary evolutionary changes, where the principle of the strongest surviving species dictates that the weak must perish. In gradual evolution, procreation is the means of ensuring survival. Offspring with two parents (or more, in rare cases) carry the genetic information of all their parents, presumably inheriting the greatest traits from each. People who inherit negative traits are feeble and fail in their attempts to stay alive. This is skillfully demonstrated in some living species, whereby a particularly strong individual succeeds in obtaining more food, becomes stronger, and ultimately drives its siblings off the nest where they will perish [18].
The community of humans used by evolutionary algorithms is known as the chromosomal populace. The traits of a person in a group are determined by a gene. A gene is used to describe each trait. An allele is a gene’s variation in value. Individuals strive to create offspring within each cycle. The most likely candidates to multiply are those with the best chances of survival. Crossover is the process of creating offspring by fusing elements of the parents. Every member of the population has the potential to experience mutation, which changes some of the chromosome allele. An individual’s sustenance strength is calculated using a fitness function that represents the goals and limitations of the issue at hand. Individuals may be eliminated after each cycle or they may live to breed another time.
Evolutionary algorithms (EAs) have been created in several classes, including the following:
Genetic algorithms that simulate the evolution of DNA.
Programming that is based on evolutionary development and simulates adaptive behavior.
Evolutionary approaches that are focused on simulating the variables involved in the activation that regulates variation.
Differential evolution, which differs in its generation method and is comparable to genetic algorithms.
Cultural evolution, which simulates how a society’s demographic changes over time and how this affects how people’s phenotypes and genetics evolve.
Co-evolution, in which formerly “stupid” people develop the traits they need to exist by cooperating or competing among themselves.
There have also been additional spontaneous evolution-related aspects modeled, for instance, dispersed (island) learning algorithm and extinction event, which maintain various populations while allowing genetic evolution to occur in each group. Additionally, factors like population movements are modeled. The improvement of evolutionary approaches has also benefited from the modeling of parasitic behavior. In this instance, people are infected with parasites. Those who are too frail pass away.
Information extraction is one of the examples of real-world scenarios where evolutionary programming has been effectively employed.
1.3.3 Optimization Method
The effectiveness of NNs is strongly influenced by the optimization technique used to compute weight adjustments. GD is a very well-liked optimization technique; however, it suffers from delayed convergence and is vulnerable to local minima. To solve these issues, GD has been improved, for instance, by including the acceleration term. Additionally, weight updates have been calculated using the objective function’s second-order derivatives. Using global optimization strategies, such as optimization, in place of local optimization algorithms is another way to enhance NN training.
1.3.3.1 Optimization
The regulation of gas pipeline transmission was one of the earliest uses of genetic algorithms that gained popularity. Multiple-fault detection, robot track identification, itinerary optimization, conformational DNA analysis constitute some of the applications of evolutionary algorithms. The emergence of neural networks is one of the computational intelligence approaches that are covered, together with other classification techniques, such as classifier systems for high-level semantic networks and mandated machine learning systems, which are used to learn regulation of pipeline processes.
1.4 Architecture Assortment
In accordance with axioms, the network with the fewest weights will, on aggregate, have the best generalization ability if multiple networks match the training set equally well. A network with an excessive number of free components may be able to accurately fit the noise present in training data as well as memorize the learning algorithm, which would result in poor generalization. Thus, overfitting can be avoided by shrinking the network by getting rid of particular weights. Therefore, the goal is to strike a compromise between the network’s intricacy and the genuine function. Architecture picking is the term used to describe this procedure. To choose the best architecture, a number of strategies, including regularization, network building, and trimming, have been devised.
Identifying the ideal structure is also considered to be part of training, in addition to determining the ideal weight values. The recognition for good is not often immediately apparent, though. A search of all feasible structures is necessary to identify the absolute optimum architecture. Meta-heuristic algorithms are employed to condense the search area. Choosing the model with the least generalization error as determined by the generalized prediction error or the network information criterion is a straightforward procedure that involves training several networks with various architectural designs.
To lessen the likelihood that the ideal design will not be discovered, several designs must be evaluated as part of this costly strategy. Alternately, the NN design can be improved through trial and error. The efficiency of a chosen structure is assessed. An alternative structure is chosen in the event that the performance is unacceptably poor. This procedure is repeated until an architecture is identified that generates an overfitting that is reasonable.
Three forms of alternative sampling techniques for design are presented:
Regularization:
The mathematical formulation that must be minimized is given a penalty term as part of the regularization process for neural networks. A number of penalty phrases have been created to dynamically lower bandwidth throughout training. Although it is an easy system to use, it is lacking in terms of penalizing large weights equally to tiny weights. In their more complex soft scale factor method, Nowlan and Hinton combined many different Rayleigh distributions to represent the distribution of weight values. Small masses are caused by narrow Gaussians, and big weights are caused by broad Gaussians.
Growing:
When the network is stuck at a minimum during development, hidden units are progressively added by network development techniques. An approximation model of a portion of the training set is created by a tiny network. To prevent resuming the function of the sampling techniques, training is essential to the success of development algorithms. Ad hoc treatment of these problems could lead to overfitting and longer training sessions.
Network Pruning:
With an enormous network as a starting point, pruning techniques for neural networks eliminate extraneous connection weights. All three types of network parameters—individual weights, hidden units, and input units—are taken into consideration for elimination. Based on metric, the choice to reduce a network property is made. Each parameter’s significance is calculated, and a pruning heuristic is employed to determine whether or not a variable is deemed an irrelevant system that can converge quite quickly with a big starting design.
The goal of every algorithm for selecting an architecture is to perform the nature of the work. Smaller networks have the following benefits in enhancing generalization benefits and reducing the fitting problem. The number of training patterns necessary to achieve a specific generalization capability depends on the generalization restrictions and the network design. Therefore, mesh nodes require fewer training examples. Additionally, a set of more basic principles can more readily characterize the information that is contained in subnetworks. Pruning networks result in fewer rules being derived from local networks or from bigger networks. Additionally, it is simpler to visualize the function of each concealed unit in subnets. Choice threshold recognition methods’ difficulty has also decreased. Due to the small test set sizes, NNs are frequently hampered by large volatility. By creating bias with network architectural reduction, this variation is decreased. Compact systems are biased because there are fewer units that can match the information because the hypothesis space is narrower.
1.4.1 Swarm Intelligence
The study of hives or swarms of social beings is where swarm intelligence (SI) got its start. Very effective optimization and clustering algorithms were developed as a result of research into the behavior of creatures in swarming. The creation of the particle swarm optimization algorithm, for instance, was inspired by simulated investigations of the elegant but erratic ballet of bird flocks, while the creation of optimizing ant colonies methods was inspired by research on the gathering habits of ants. In order to position the particles toward an ideal solution, a particle must tend to make use of both its own perfect situation and the best positions of its neighbors. The result is that particles “fly” toward an ideal state while still looking in many different directions for the best available answer. Each particle’s effectiveness (i.e., its “closeness” to the global minimum) is evaluated using a predetermined fitness value that is connected to the issue at hand.
Numerous ant colony studies have been influenced by the modeling of ant pheromone deposition in their pursuit of the quickest routes to feedstuffs. Ant colony optimization is also used to solve the multi-objective issue, color graphs, optimize routing in network technologies, and schedule tasks. Algorithms for clustering and structural optimization were created as a result of research into the construction of ant and bee nests.
1.4.2 Artificial Immune Systems
Impressive pattern recognition skills are utilized by the natural immune system (NIS) to discriminate between body-belonging particles and external particles that are ingested. The NIS’s adaptive character is on display as it interacts with antigens, learning the antigen’s composition to respond to it more quickly.
There are four types of the NIS that have been studied:
According to the traditional understanding of the immune system, cytokines generated in the lymphoid organs are used to discriminate between individuality. These cells acquire the ability to attach to an antigen.
The replication process used by an active B-cell to create antibodies under the search and optimization paradigm. The clones that are created are likewise altered.
The danger theory, which postulates that the immune system can tell a risky antigen apart from a non-threatening one.
Network theory, where a system of B-cells is presumptively formed. A B-cell reacts to an antigen that is associated with the system and gets active.
An artificial immune system (AIS) is primarily used to address information processing issues, carry out classifiers, and aggregate datasets. An AIS simulates some of the characteristics of a NIS. A detection system, including fraud and software malware, is one of the key applications for AISs.
1.5 Myths About Computational Intelligence
There are some misconceptions about computational intelligence. First, contrary to what some private vendors claim, using CI technologies does not require a supercomputer, a large number of money, or a multidisciplinary team in order to get results. It is not necessary to have a supercomputer or a parallel processing device in order to use CI technologies effectively. The hardware foundation for the majority of operational and interface initiatives is a computer. Therefore, issues that would ordinarily be impractical to tackle can be handled using very simple hardware and software techniques. Some issues can be tackled using machine learning technologies that cannot be solved by conventional methods.
The idea that CI tools can handle the majority of issues by themselves is a misconception as well. They frequently are not suitable for issues needing exact computations. For instance, it is improbable that the neural network is going to be adequately balanced. The claim that artificial neural networks may be used without any scripting is another one that falls under the category of largely myth. This is, at best, deceptive. The training of a neural net is not merely asserting designs; rather, it trains and runs on incoming data and is in accordance with a set of rules that update the weights connecting the processing units or nodes.
However, computing is necessary in the reality of neural network implementations to move from the specification of the problem to a solution. The need for rebooting is greatly reduced by neural network technologies. Once the issue has been identified, it is not uncommon to reuse the network code multiple times while adjusting to the network real-time settings for data preparation.
In addition, it is truly the case that computational intelligence techniques like neural networks can be crucial in solving a variety of types of difficulties that are challenging or difficult to resolve using currently available methods. Most of the code is often used during filtering and other number crunching methods to create pattern files for presentation to the network. Some other substantial portion is frequently taken up by interpreting and displaying the results. Another misconception concerning implementations of neural networks and soft computing is that you need to comprehend neural biology or biological genetics in order to comprehend them.
Neural network and evolutionary computation methods can actually be viewed as a suite of features in the CI analytics arsenal by the majority of architects and computer scientists. Additionally, there is a strong case to be made for the idea that neural networks are technically related to analog technology in the same way that they are related to neurology. It is a misconception that fuzzy logic is genuinely fuzzy or inaccurate. Case formulation for process variables serve as the inputs to a fuzzy system. Similar to sharp outcomes, information from a fuzzy system can, for example, be used as accurate inputs to control systems.
The idea that fuzzy logic is only an additional form of probabilities is another misconception about it. It is not. Fuzzy logic is related to nonstatistical uncertainty, whereas likelihood deals with empirical unpredictability. The idea that optimization occurs is fiction, to sum up. Although I’m saying this somewhat jokingly, it is vital to understand how infrequently a real-world CI implementation finds the complete best of everything. If the maximum is known, a decision to arrive inside a specific area of it is supported. If there are several optima, the design involves the process of locating the ideal one or, in that case, all of the multiple optima.
1.6 Supervised Learning in Computational Intelligence
All species might be motivated by competence. The quality of life will not increase if functionality is not improved. According to this, when designing an ANN, functionality is the most crucial factor to take into account. The effectiveness of an ANN is evaluated not only by the steadiness attained by the system, but also by factors like computational complexity and convergence properties. These metrics, along with others that evaluate the effectiveness, specifically mention supervised nodes. Numerous aspects that affect connection speeds must be carefully taken into account while designing NNs for optimum level. The neural network-based research enables one to design the system mostly carried out by adhering to the rules of the spontaneous impressions of the experienced systems. It was important to better comprehend how NNs functioned and to open the “black box” thanks to the numerous theoretical analyses of NNs that have been conducted. These discoveries aided in the development of more effective NNs.
1.6.1 Performance Measures
Complexity, accuracy, and convergence are the three categories under which NN evaluation metrics are presented in this section.
1.6.1.1 Accuracy
A crucial component of neural network learning is generalization. The eventual aim of NN acquisition is to create a learning with minimal generalization error. For multiclass classification, an additional error measure is necessary because the MSE is not a reliable indicator of accuracy on its own. The proportion of successfully categorized patterns is used as an efficiency indicator for classification tasks. The structure may be accurate across the quantity of right categories despite the somewhat high results, which is why the MSE is not an appropriate measure.
Performance of the model is a crucial component of NN efficiency. The NN memorizes the learning algorithm when a supervised learning is overfit, which impairs its capacity to generalize. That is, overfit NNs are unable to forecast accurate results for large datasets that were not visible during learning. The parameters, as a result, obtain too many unimportant input components and hidden units. The learning algorithm, as well as the noise in the learning algorithm of the NN, is taught for an excessively long time.
To identify the moment of overfitting, estimates of generalization error throughout retraining might be used. Studies of training and generalization profiles led to the development of the most straightforward method for determining data of overfitting. A typical representation of learning and extension errors as a consequence of training iteration is shown in Figure 1.5. Beginning with the first training session, both the training and generalization defects start to decline—typically dramatically. There is a moment, especially for large NNs, where the learning rate continues to go down while the generalization error begins to go up. As soon as a generalization mistake increases, learning should end.
Figure 1.5 Illustration of overfitting.
When the learning rate and the error diverge, overfitting is present because the prediction error grows while the learning rate decreases. The learning rate should be higher than unity for categorization situations where the proportion of correctly categorized sequences is used as a gauge of performance. It is significant to highlight that measuring the accuracy of an NN solely on the basis of training error is insufficient. These two mistakes must be taken into account.
1.6.1.2 Complexity
The network architecture:
This factor directly affects how complicated an NN is to compute. The broader the structure, the more teaching parameters are required for each pattern display required to anticipate outcomes of the learning rate.
The size of the training set:
More designs are made available for the learning rate of the system. As a result, learned computations are done overall per epoch.
Complexity of the optimization method:
To enhance the accuracy and convergence properties of NNs, numerous complexes that focus on the key have been devised. However, the intricacy comes at the expense of more difficult computations to calculate weight adjustments. The amount of training time required to reach a certain training or generalization error is typically expressed in terms of the number of epochs. The calculation time is typically not a reliable indicator of training time or computing cost when comparing various learning techniques.
1.6.1.3 Convergence
The capacity of the network to converge to predetermined erroneous levels can be used to explain the convergence characteristics of an NN. The structure accomplished in attaining a certain error is used to represent a network’s capacity to converge to that mistake. Although this is an empirical technique, several network topologies have undergone comprehensive theoretical research.
