Fuzzy Logic Applications in Computer Science and Mathematics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
FUZZY LOGIC APPLICATIONS IN COMPUTER SCIENCE AND MATHEMATICSTICS The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering. The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes. Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book. Audience This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.
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Seitenzahl: 388
Veröffentlichungsjahr: 2023
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Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Decision Making Using Fuzzy Logic Using Multicriteria
1.1 Introduction
1.2 Fuzzy Logic
1.3 Decision Making
1.4 Literature Review
1.5 Conclusion
Acknowledgment
References
2 Application of Fuzzy Logic in the Context of Risk Management
2.1 Introduction
2.2 Objectives of Risk Management
2.3 Improved Risk Estimation
2.4 Threat at Quantification Matrix
2.5 Fundamental Definitions
2.6 Fuzzy Logic
2.7 Risk Related to Fuzzy Matrix
2.8 Conclusion
Bibliography
3 Use of Fuzzy Logic for Controlling Greenhouse Environment: A Study Through the Lens of Web Monitoring
3.1 Introduction
3.2 Design (Hardware)
3.3 Programming Arduino Mega Board
3.4 Implementation of a Prototype
3.5 Results
3.6 Conclusion
Bibliography
4 Fuzzy Logics and Marketing Decisions
4.1 Introduction
4.2 Literature
4.3 Conclusion
4.4 Further Studies
References
5 A Method for Ranking Fuzzy Numbers Based on Their Value, Ambiguity, Fuzziness, and Vagueness
5.1 Introduction
5.2 Preliminaries
5.3 The Designed Method
5.4 Validate the Reasonableness of the Suggested Ranking Algorithm
5.5 Comparative Analysis and Numerical Examples
5.6 Application
5.7 Conclusions
References
6 Evacuation of Attributes to Translucent TNSET in Mathematics Using Rough Topology
6.1 Introduction
6.2 Basic Concepts of Rough Topology
6.3 Algorithm
6.4 Information System
6.5 Working Procedure
6.6 Conclusion
References
7 Design of Type-2 Fuzzy Controller for Hybrid Multi-Area Power System
7.1 Introduction
7.2 Plant Model
7.3 Controller Design
7.4 Levenberg–Marquardt Algorithm
7.5 Optimization of Controller Parameters Using CASO Algorithm
7.6 Result and Analysis
7.7 Conclusion
Appendix
References
8 Alzheimer’s Detection and Classification Using Fine-Tuned Convolutional Neural Network
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 Implementation and Results
8.5 Conclusion
References
9 Design of Fuzzy Logic-Based Smart Cars Using Scilab
9.1 Introduction
9.2 Literature Survey
9.3 Proposed Fuzzy Inference System for Smart Cars
9.4 Implementation Details and Results
9.5 Conclusion and Future Work
References
10 Financial Planning and Decision Making for Students Using Fuzzy Logic
10.1 Introduction
10.2 Literature Review
10.3 System Architecture
10.4 Conclusion and Future Scope
References
11 A Novel Fuzzy Logic (FL) Algorithm for the Automatic Detection of Oral Cancer
11.1 Introduction
11.2 Image Enhancement
11.3 Gabor Transform
11.4 Image Transformation
11.5 Adaptive Networks: Architecture
11.6 Results and Discussions
11.7 Conclusion
Bibliography
12 A Study on Decision Making of Difficulties Faced by Indian Workers Abroad by Using Rough Topology
12.1 Introduction
12.2 Fundamental Idea of Rough Topology
12.3 Algorithm
12.4 Information System
12.5 Working Procedure
12.6 Conclusion
References
13 Case Study on Fuzzy Logic: Fuzzy Logic-Based PID Controller to Tune the DC Motor Speed
13.1 Introduction
13.2 Literature Review
13.3 Design of Fuzzy-Based PID Controller
13.4 Experimental Work and Results Analysis
13.5 Conclusion and Future Scope
References
14 Application of Intuitionistic Fuzzy Network Using Efficient Domination
14.1 Introduction
14.2 Efficient Domination in Intuitionistic Fuzzy Graph (IFG)
14.3 Main Frame Work
14.4 Secret Key
14.5 Illustration
14.6 Conclusion
References
15 Analysis of Parameters Related to Malaria with Comparative Study on Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
15.1 Introduction
15.2 Parameters of Malaria
15.3 Fuzzy Cognitive Map
15.4 Neutrosophic Cognitive Map
15.5 Comparison and Discussion
15.6 Conclusion
References
16 Applications of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Analysis of Dengue Fever
16.1 Introduction
16.2 Parameters of Dengue
16.3 Fuzzy Cognitive Maps
16.4 Neutrosophic Cognitive Map
16.5 Comparison and Discussion
16.6 Conclusion
References
17 A Comprehensive Review and Analysis of the Plethora of Branches of Medical Science and Bioinformatics Based on Fuzzy Logic
17.1 Introduction
17.2 Previous Work
17.3 Fuzzy Logic in Medical Fields and Bioinformatics
17.4 Review of Published Work and In-Depth Analysis
17.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Risk level (through the lens of a case study).
Table 2.2 Suggestions for rating the seriousness of mishaps.
Table 2.3 Suggested mishap probability levels.
Table 2.4 Test input design for security through fuzzy graph.
Chapter 5
Table 5.1 Rank order of fuzzy numbers in Example 5.5.1.
Table 5.2 Rank order of fuzzy numbers in Example 5.5.2.
Table 5.3 Rank the order of fuzzy numbers in Example 5.5.3.
Table 5.4 Rank the fuzzy numbers in Example 5.5.4 in order.
Table 5.5 Rank the fuzzy numbers in Example 5.5.5 in order.
Table 5.6 Rank the fuzzy numbers in Example 5.5.6 in order.
Table 5.7 Rank the fuzzy numbers in Example 5.5.7 in order.
Table 5.8 Linguistic variables for ratings and weights [42].
Table 5.9 Decision matrix of candidates versus decision makers opinions and ...
Table 5.10 Fuzzy numbers for the five investment avenues.
Chapter 6
Table 6.1 Options recorded by respondents corresponding to the attributes re...
Chapter 7
Table 7.1 Rule base for type reduction.
Table 7.2 Performance analysis of two area hybrid system using IT2FLC based ...
Table 7.3 CASO tuned PID and FOPID controller parameters.
Table 7.4 Performance results of 2 area hybrid system using IT2FLC based PID...
Chapter 8
Table 8.1 Summary of various activation functions.
Table 8.2 Performance metrics of ADNet without SMOTE.
Table 8.3 Various performance metrics for ADNet.
Chapter 9
Table 9.1 External factors.
Table 9.2 Atmospheric conditions.
Table 9.3 Traffic.
Table 9.4 Obstacle distance.
Table 9.5 Obstacle position.
Table 9.6 FAM Table.
Table 9.7 Speed.
Table 9.8 Direction.
Chapter 10
Table 10.1 Fuzzy rules.
Chapter 11
Table 11.1 Shows the performance of the adaptive median filter.
Table 11.2 Performance metrics, ANFIS classifier with different classificati...
Chapter 12
Table 12.1 Statistical data received from applicants who stayed abroad from ...
Chapter 13
Table 13.1 Comparison table.
Table 13.2 Defining membership functions range for error.
Table 13.3 Defining membership functions range.
Table 13.4 Defining membership functions range for desired speed.
Table 13.5 FAM table for fuzzy tuned PID controller.
Table 13.6 Response of proposed fuzzy-PID.
Table 13.7 Comparison between same technique and proposed work.
Chapter 14
Table 14.1 Membership values of vertex degree and edge degree.
Table 14.2 Degree membership values of the edges.
Chapter 15
Table 15.1 Comparison results of FCM and NCM on malaria.
Chapter 16
Table 16.1 Comparison results of FCM and NCM on dengue.
Chapter 17
Table 17.1 Review of various diseases using the fuzzy system.
List of Illustrations
Chapter 2
Figure 2.1 Evaluation of risk as it occurs.
Figure 2.2 Curve used to estimate risk.
Figure 2.3 Scaling and bandwidth approximation errors.
Figure 2.4 Risk under the tolerance limit.
Figure 2.5 Multi-valued vs Boolean logic.
Figure 2.6 Classification of fuzzy age.
Figure 2.7 Fundamental of fuzzy structure.
Figure 2.8 Risk graph model.
Figure 2.9 Membership function (risk management).
Figure 2.10 Graph regarding fuzzy risk model.
Figure 2.11 Test for safety of fuzzy model by using centroid, bisector and MOM...
Figure 2.12 Aggregation of fired membership functions.
Chapter 3
Figure 3.1 Framework of the block diagram.
Figure 3.2 Device for measuring water content.
Figure 3.3 (a) DHT11. (b) Circuit diagram.
Figure 3.4 Chart for the fuzzification and defuzzification processes.
Figure 3.5 (a) Temperature. (b) Relative humidity.
Figure 3.6 Web interface.
Figure 3.7 Web interface (flowchart).
Figure 3.8 Prototype setup for greenhouse.
Figure 3.9 Ranges are defined at set points (a), humidity, (b) temperature.
Figure 3.10 Distinct range of the set points: (a) humidity, (b) temperature.
Figure 3.11 Set points in the defined range (a) humidity, (b) temperature.
Chapter 5
Figure 5.1 Fuzzy numbers
μ and ν
are represented graphically in Exam...
Figure 5.2 Graphical representation of fuzzy numbers
µ
and
ν
in Exam...
Figure 5.3 Graphical representation of TrFNs
µ
and
ν
in Example 5.5....
Figure 5.4 Graphical representation of TrFNs
µ
and
ν
in Example 5.5....
Figure 5.5 Fuzzy numbers
µ
,
ν
, and
ρ
are graphically represente...
Figure 5.6 Fuzzy numbers
µ
,
ν
, and
ρ
are graphically represente...
Figure 5.7 Fuzzy numbers
µ
,
ν
and
ρ
are graphically represented...
Chapter 7
Figure 7.1 Block diagram of the hybrid two area interconnected power system [2...
Figure 7.2 Structure of PID controller.
Figure 7.3 Structure of FOPID controller.
Figure 7.4 Illustration of (a) type-2 fuzzy membership function (b) triangular...
Figure 7.5 Block diagram representation of IT2FLC.
Figure 7.6 Flow chart of Levenberg Marquardt algorithm.
Figure 7.7 Two area hybrid system controlled by PID, IT2FLC based PID and IT2F...
Figure 7.8 Two area hybrid system controlled by PID, IT2FLC based PID and IT2F...
Chapter 8
Figure 8.1 Normal brain.
Figure 8.2 Alzheimer disease brain.
Figure 8.3 (a) Nondemented. (b) Moderately demented. (c) Mild demented. (d) Ve...
Figure 8.4 CNN architecture.
Figure 8.5 Proposed architecture ADNET.
Figure 8.6 Training and validation accuracy, AUC and loss for 100 epochs.
Figure 8.7 Confusion matrix of ADNet with SMOTE.
Chapter 9
Figure 9.1 Recent trend analysis of fuzzy logic.
Figure 9.2 GUI Interface of the FIS in Scilab.
Figure 9.3 Fuzzy inputs in Scilab.
Figure 9.4 Membership functions for the input variable atmospheric conditions.
Figure 9.5 Design of smart car using fuzzy logic framework.
Figure 9.6 Plot of the five input membership functions using Scilab.
Figure 9.7 Plot of the two output membership functions using Scilab.
Chapter 10
Figure 10.1 Trend analysis of fuzzy logic papers in the past decades.
Figure 10.2 Proposed architecture of fuzzy finance planning system.
Figure 10.3 Input membership functions.
Figure 10.4 Membership variables of necessity.
Figure 10.5 Membership variables of the membership function cost percentage.
Figure 10.6 Membership variables of the membership function quality.
Figure 10.7 Fuzzy output of the membership functions.
Chapter 11
Figure 11.1 ANFIS structure.
Chapter 13
Figure 13.1 Block diagram PID controller.
Figure 13.2 DC motor model.
Figure 13.3 Block diagram.
Figure 13.4 Flow graph of fuzzy controller.
Figure 13.5 Membership function for error.
Figure 13.6 Membership function for change in error.
Figure 13.7 Membership function for change in error.
Figure 13.8 FAM table for fuzzy controller.
Figure 13.9 Simulation of fuzzy controller in LABVIEW.
Figure 13.10 Fuzzy based PID controllers.
Figure 13.11 Flow chart of fuzzy block.
Figure 13.12 FPID controller flowchart.
Figure 13.13 Fuzzy-based PID controller in LABVIEW.
Figure 13.14 Step response for fuzzy-based PID controllers.
Chapter 14
Figure 14.1 Efficient domination of IFG.
Figure 14.2 IFN subnetwork-1.
Figure 14.3 IFN subnetwork-2.
Figure 14.4 IFN
r
th subnetwork.
Figure 14.5 Encrypted IFN network with minimum edges.
Figure 14.6 Encrypted IFN with moderate edges.
Figure 14.7 Encrypted IFN with more than moderate edges.
Figure 14.8 Illustration IFN subnetwork-1.
Figure 14.9 Illustration IFN subnetwork-2.
Figure 14.10 Illustration IFN subnetwork-3.
Figure 14.11 Illustration IFN subnetwork-4.
Figure 14.12 Illustration IFN subnetwork-5.
Figure 14.13 Illustration encrypted IFN with secret number-10810.
Chapter 15
Figure 15.1 Fuzzy cognitive maps on malaria.
Figure 15.2 Neutrosophic cognitive maps on malaria.
Chapter 16
Figure 16.1 Fuzzy cognitive maps on dengue.
Figure 16.2 Neutrosophic cognitive maps on dengue.
Chapter 17
Figure 17.1 Structure of boolean and fuzzy logic.
Figure 17.2 Architecture of fuzzy process.
Figure 17.3 Flow diagram of the fuzzy process to diagnosis the disease.
Guide
Cover
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Fuzzy Logic Applications in Computer Science and Mathematics
Edited by
Rahul KarDac-Nhuong LeGunjan MukherjeeBiswadip Basu Mallik
and
Ashok Kumar Shaw
This edition first published 2023 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© 2023 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 978-1-394-17453-9
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
The prime objective of developing this book was to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering. The book caters to a certain level of professional knowledge, academicians, students, and researchers. The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and also is related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes. Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated through many highly regarded research papers. Each chapter has been produced in a very lucid manner, with grading from simple to complex in an effort to accommodate different audiences.
The application-oriented approach is the unique feature of this book. Apart from the theoretical discussion, the problems and the allied case studies concerned with the topics discussed in this book will be of great interest to a broad audience. The problems and the case studies furnished in this book are worthwhile to researchers and academicians, as well. This book comprises state-of-the-art information on a wide range of various subjects, all directly or indirectly connected to the overarching topic.
Fuzzy logic and its application have evolved significantly and, through many research paths, have arrived at the current stage. With concern paid to the students of different types of engineering, this book also addresses some additional aspects. Primarily the book focuses on:
The myriad modern research information in the field of computational intelligence, presented with references to many published papers
The pertinent information and research in the field of fuzzy systems, its different variants, and evolutionary computing
The future research directions in the field of fuzzy logic-based computational intelligence, which provides an effective means of research in the field of classification of items, from different species and so forth
Providing a compact treatise on the fuzzy-based computational intelligence and how it applies to evolutionary computing
The material of this book was developed and arranged so that readers can easily grasp the fundamental concepts of the subject and gradually move to more advanced levels through functional assessments of the matter in both broad and analytical ways. The target readership includes researchers, professionals, and students willing to pursue their career further in the field of computation in the fuzzy domain.
We express our sincere thanks with ample acknowledgment to all our colleagues, friends, and students for their invaluable suggestions and feedback in the development of this book, including the provision of more important and relevant information. We must offer our heartfelt gratitude to our family members, for without their support and endurance, this book would have been an impossible task. Lastly, we are very much grateful to the editors at Scrivener and Wiley.
We wish every reader an insightful, perceptional, and informative journey into this book, the world of fuzzy logic systems, and its application paradigm.
The Editors
Rahul Kar, Dac-Nhuong Le, Gunjan Mukherjee, Biswadip Basu Mallik and Ashok Kumar Shaw
July 2023
