Dynamical Behaviors of Multiweighted Complex Network Systems E-Book

Jin-Liang Wang

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 208

Veröffentlichungsjahr: 2024

Beschreibung

Highly comprehensive resource for studying neural networks, complex networks, synchronization, passivity, and associated applications

Dynamical Behaviors of Multiweighted Complex Network Systems discusses the dynamical behaviors of various multiweighted complex dynamical networks, with detailed insight on synchronization for directed and undirected complex networks (CNs) with multiple state or delayed state couplings subject to recoverable attacks, along with passivity and synchronization for coupled neural networks with multi-weights (CNNMWs) by virtue of devised proportional-integral-derivative (PID) controllers.

The book also investigates finite-time synchronization (FTS) and H-infinity synchronization for two types of coupled neural networks (CNNs) and focuses on finite-time passivity (FTP) and finite-time synchronization (FTS) for complex dynamical networks with multiple state/derivative couplings based on the proportional-derivative (PD) control method. Final chapters consider finite-time output synchronization and H-infinity output synchronization problems, and multiple weighted coupled reaction-diffusion neural networks (CRDNNs) with and without coupling delays.

Other topics covered in Dynamical Behaviors of Multiweighted Complex Network Systems include:

• Criteria of FTP for complex dynamical networks with multiple state couplings (CDNMSCs), formulated by utilizing the PD controller
• Finite-time passivity (FTP) concepts for the spatially and temporally systems with different dimensions of output and input
• FTS and finite time H-infinity synchronization problems for CDNs with multiple state/derivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes
• Adaptive output synchronization and output synchronization of CDNs with multiple output or output derivative couplings, and other adaptive control schemes

Enabling readers to understand foundational concepts and grasp the latest research, Dynamical Behaviors of Multiweighted Complex Network Systems is essential for all who study neural networks, complex networks, synchronization, passivity, and their applications.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Authors

Preface

Acknowledgments

1 Synchronization for Complex Networks with Multiple Weights Under Recoverable Attacks

1.1 Introduction

1.2 Preliminaries

1.3 Synchronization of CNMSCs Under Recoverable Attacks

1.4 Synchronization of CNMDSCs Under Recoverable Attacks

1.5 Numerical Examples

1.6 Conclusion

References

2 Passivity and Synchronization for Coupled Neural Networks with Multiweights Under PD and PI Control

2.1 Introduction

2.2 Preliminaries

2.3 PD Control for Passivity and Synchronization of the CNNMWs

2.4 PI Control for Passivity and Synchronization of the CNNMWs

2.5 Numerical Examples

2.6 Conclusion

References

3 Output Synchronization for Complex Networks with Multiple Output or Output Derivative Couplings

3.1 Introduction

3.2 Output Synchronization of CDNs with Multiple Output Couplings

3.3 Output Synchronization of CDNs with Multiple Output Derivative Couplings

3.4 Numerical Examples

3.5 Conclusion

References

4 PD Control for Finite-Time Passivity and Synchronization of Multiweighted Complex Networks

4.1 Introduction

4.2 Preliminaries

4.3 PD Control for the FTP and FTS of the CDNMSCs

4.4 PD Control for the FTP and FTS of the CDNMDCs

4.5 Numerical Examples

4.6 Conclusion

References

5 Finite-Time Synchronization and Synchronization for Coupled Neural Networks with Multistate or Multiderivative Couplings

5.1 Introduction

5.2 Preliminaries

5.3 FTS and Finite-Time Synchronization for CNNs with Multistate Couplings

5.4 FTS and Finite-Time Synchronization for CNNs with Multiderivative Couplings

5.5 Numerical Examples

5.6 Conclusion

References

6 Finite-Time Synchronization and Synchronization of Multiweighted Complex Networks with Adaptive State Couplings

6.1 Introduction

6.2 Preliminaries

6.3 Finite-Time Synchronization and Synchronization of Multiweighted Complex Dynamical Networks with Adaptive State Couplings

6.4 Finite-Time Synchronization and Synchronization of Multiweighted Complex Dynamical Networks with Coupling Delays and Adaptive State Couplings

6.5 Numerical Examples

6.6 Conclusion

References

7 Finite-Time Output Synchronization and Output Synchronization of Coupled Neural Networks with Multiple Output Couplings

7.1 Introduction

7.2 Preliminaries

7.3 Finite-Time Output Synchronization of CNNMOC

7.4 Finite-Time Output Synchronization of CNNMOC

7.5 Numerical Examples

7.6 Conclusion

References

8 Finite-Time Passivity and Synchronization of Coupled Reaction–Diffusion Neural Networks with Multiple Weights

8.1 Introduction

8.2 Preliminaries

8.3 Finite-Time Passivity and Synchronization of CRDNNs with Multiple Weights

8.4 Finite-Time Passivity and Synchronization of CRDNNs with Multiple Coupling Delays

8.5 Numerical Examples

8.6 Conclusion

References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1

Figure 1.2 The CNMSCs (1.38) under the successful but recoverable attacks, w...

Figure 1.3

Figure 1.4 The CNMDSCs (1.39) under the successful but recoverable attacks, ...

Chapter 2

Figure 2.1 Change curves of (a) (b) (c) in the network (2.39) under th...

Figure 2.2 Evolutions of in the network (2.39) under the PD controller (2....

Figure 2.3 Change curves of in network (2.40) under the PI controller (2.2...

Figure 2.4 Evolutions of in network (2.40) under the PI controller (2.23)....

Chapter 3

Figure 3.1 .

Figure 3.2 .

Figure 3.3 .

Figure 3.4 .

Chapter 4

Figure 4.1 The evolution processes of and in the network (4.33) under th...

Figure 4.2 The changing curves of in the network (4.33) under the PD contr...

Figure 4.3 The evolution processes of and in the network (4.34) under th...

Figure 4.4 The changing curves of in the network (4.34) under the PD contr...

Chapter 5

Figure 5.1 .

Figure 5.2 .

Figure 5.3 and .

Figure 5.4 .

Figure 5.5 .

Figure 5.6 .

Figure 5.7 and .

Figure 5.8 .

Chapter 6

Figure 6.1 .

Figure 6.2 Adaptive coupling weights.

Figure 6.3 .

Figure 6.4 Adaptive coupling weights.

Figure 6.5 .

Figure 6.6 Adaptive coupling weights.

Figure 6.7 .

Figure 6.8 Adaptive coupling weights.

Chapter 7

Figure 7.1 ,

Figure 7.2 , 

Figure 7.3 ,

Figure 7.4 , 

Chapter 8

Figure 8.1 .

Figure 8.2 .

Figure 8.3 .

Figure 8.4 .

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Authors

Preface

Acknowledgments

Begin Reading

Index

End User License Agreement

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief

Moeness Amin

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Ekram Hossain

Brian Johnson

Hai Li

James Lyke

Joydeep Mitra

Desineni Subbaram Naidu

Tony Q. S. Quek

Behzad Razavi

Thomas Robertazzi

Diomidis Spinellis

Dynamical Behaviors of Multiweighted Complex Network Systems

Jin-Liang Wang

Tiangong University, Tianjin

China

Shun-Yan Ren

University of Electronic Science and Technology of China, Chengdu

China

Huai-Ning Wu

Beihang University, Beijing

China

Tingwen Huang

Shenzhen University of Advanced Technology, Shenzhen

China

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Library of Congress Cataloging-in-Publication Data

Names: Wang, Jin-Liang (Professor), author. | Ren, Shun-Yan, author. | Wu, Huai-Ning, author. | Huang, Tingwen, author.

Title: Dynamical behaviors of multiweighted complex network systems / Jin-Liang Wang, Shun-Yan Ren, Huai-Ning Wu, Tingwen Huang.

Description: Hoboken, New Jersey : Wiley-IEEE Press, [2024] | Includes index.

Identifiers: LCCN 2024002858 (print) | LCCN 2024002859 (ebook) | ISBN 9781394228614 (cloth) | ISBN 9781394228645 (adobe pdf) | ISBN 9781394228638 (epub)

Subjects: LCSH: Neural networks (Computer science) | Dynamics.

Classification: LCC QA76.87 .W34 2024 (print) | LCC QA76.87 (ebook) | DDC 006.3/2–dc23/eng/20240314

LC record available at https://lccn.loc.gov/2024002858

LC ebook record available at https://lccn.loc.gov/2024002859

Cover Design: WileyCover Image: © Xuanyu Han/Getty Images

This book can help readers understand the research background and grasp the latest research developments for multiweighted complex network systems.

About the Authors

Jin-Liang Wang received the PhD degree in control theory and control engineering from the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, in January 2014. From 2014 to 2016, he was a lecturer and from 2017 to 2019, he was an associate professor with the School of Computer Science and Technology, Tiangong University, Tianjin, China, where he has been a professor since 2020. As the first author, he has published 6 English academic monographs in the Springer, Elsevier, and IEEE, and 48 SCI-indexed journal papers (including 34 in Automatica and IEEE Transactions), which have been cited in the SCI-indexed journals by other researchers more than 1800 times; 11 first author papers were recognized as the ESI Highly Cited Papers, and 1 first author paper was selected as the ESI Hot Paper. His current research interests include complex networks, coupled neural networks, multiagent systems, and fractional-order systems. Professor Wang was a managing guest editor for the special issue on Dynamical Behaviors of Coupled Neural Networks with Reaction-Diffusion Terms: Analysis, Control and Applications in Neurocomputing. He also serves as an associate editor for the Neurocomputing, the International Journal of Adaptive Control and Signal Processing, IEEE Systems, Man, and Cybernetics Magazine, and Complex & Intelligent Systems.

Shun-Yan Ren received the BS degree in mathematics from Langfang Normal University, Langfang, China, in 2007, and the PhD degree in mechanical design and theory from Tiangong University, Tianjin, China, in 2021. She is currently a postdoctoral researcher with the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China. Her current research interests include passivity, consensus, complex networks, and multiagent systems.

Huai-Ning Wu received the BE degree in automation from Shandong Institute of Building Materials Industry, Jinan, China, and the PhD degree in control theory and control engineering from Xi’an Jiaotong University, Xi’an, China, in1992 and 1997, respectively. From 1997 to 1999, he was a postdoctoral research fellow with the Department of Electronic Engineering, Beijing Institute of Technology, Beijing, China. Since 1999, he has been with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He is currently a professor with Beihang University. His current research interests include robust and fault-tolerant control, intelligent control, distributed parameter systems, and human-in-the-loop systems. Dr. Wu serves as an associate editor for IEEE Transactions on Fuzzy Systems and IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Tingwen Huang received the BS degree in mathematics from Southwest Normal University, Chongqing, China, in 1990, the MS degree in applied mathematics from Sichuan University, Chengdu, China, in 1993, and the PhD degree in mathematics from Texas A&M University, College Station, TX, USA, in 2002. From 1994 to 1998, he was a lecturer with Jiangsu University, Zhenjiang, China. In 2003, he was a visiting assistant professor with Texas A&M University. He was an assistant professor from 2003 to 2009 and an associate professor from 2009 to 2013 with Texas A&M University at Qatar, Doha, Qatar, where he has been a professor since 2013. He is currently a professor with Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China. His current research interests include neural networks, complex networks, chaos and dynamics of systems, and operator semigroups and their applications. Dr. Huang is an associate editor for IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Artificial Intelligence, and IEEE Transactions on Emerging topics in Computational Intelligence.

Preface

Recently, numerous researchers have turned their attention to the dynamical behaviors of complex dynamical networks (CDNs) including the synchronization, stability, and passivity. However, most of existing results on dynamical behaviors are all based on network models with single weight. It is well known that many real-world networks should be described by CDNs with multiweights, such as public traffic roads networks, social networks, intercity population flow networks, and so on. Therefore, it is meaningful to further study the dynamical behaviors for multiweighted CDNs (MWCDNs).

The purpose of this book is to introduce recent research work on analysis and control of dynamical behaviors for MWCDNs. The book is structured as follows:

Chapter 1 respectively takes the synchronization into consideration for directed and undirected complex networks (CNs) with multiple state or delayed state couplings subject to recoverable attacks. By employing inequality techniques, choosing appropriate Lyapunov functional, and adopting the designed state feedback controller, two criteria of the synchronization are developed for the directed CN with multiple state couplings (CNMSCs). Moreover, the synchronization of CNMSCs is also discussed for the case that the network topology is undirected. In addition, two types of CNs with multiple delayed state couplings are also proposed, and several criteria of synchronization are formulated for these networks.

Chapter 2 mainly discusses the passivity and synchronization for a coupled neural networks with multiweights (CNNMWs) by virtue of devised proportional-integral-derivative (PID) controllers. On the one hand, several passivity criteria for the CNNMWs are put forward on the basis of suitable Lyapunov functional, inequality techniques, and devised proportional-derivative (PD) controller, and the output-strict passivity is employed to deal with the synchronization of the CNNMWs. On the other hand, the passivity of the CNNMWs is also investigated by choosing appropriate proportional-integral (PI) controller, and a criterion of synchronization is developed for the CNNMWs.

Chapter 3 tackles the output synchronization problem for CDNs with multiple output or output derivative couplings in detail. With the help of inequality techniques and Lyapunov functionals, an output synchronization criterion is presented for CDNs with multiple output couplings (CDNMOCs). To ensure the output synchronization of CDNMOCs, an adaptive control scheme is also devised. Similarly, we also take into account the adaptive output synchronization and output synchronization of CDNs with multiple output derivative couplings.

Chapter 4 respectively focuses on finite-time passivity (FTP) and finite-time synchronization (FTS) for CDNs with multiple state/derivative couplings on the basis of PD control method. Several criteria of FTP for CDNs with multiple state couplings (CDNMSCs) are formulated by utilizing the PD controller and constructing an appropriate Lyapunov function. Furthermore, FTP is further used to investigate the FTS in CDNMSCs under the PD controller. In addition, the FTP and FTS for CDNs with multiple derivative couplings (CDNMDCs) are also studied by exploiting the PD control method and some inequality techniques.

Chapter 5 investigates the FTS and finite-time synchronization for two types of coupled neural networks (CNNs): the cases with multistate couplings and multiderivative couplings. By designing appropriate state feedback controllers and parameter adjustment strategies, some FTS and finite-time synchronization criteria for CNNs with multistate couplings are derived. In addition, the FTS and finite-time synchronization problems for CNNs with multiderivative couplings are further considered by employing state feedback control approach and selecting suitable parameter adjustment strategies.

Chapter 6 introduces two kinds of multiweighted and adaptive state coupled CNs with or without coupling delays. First, we develop the appropriate state feedback controller and adaptive law in order to guaranteeing that the proposed network models without coupling delays can be finite-time synchronized and synchronized. Furthermore, for the multiweighted CNs with coupling delays and adaptive state couplings, some finite-time synchronization and synchronization criteria are developed by choosing appropriate adaptive law and controllers.

Chapter 7 focuses the finite-time output synchronization and output synchronization problems for coupled neural networks with multiple output couplings (CNNMOCs), respectively. By choosing appropriate state feedback controllers, several finite-time output synchronization and output synchronization criteria are proposed for the CNNMOC. Moreover, a coupling-weight adjustment scheme is also developed to ensure the finite-time output synchronization and output synchronization of CNNMOC.

Chapter 8 introduces two kinds of multiple weighted coupled reaction-diffusion neural networks (CRDNNs) with and without coupling delays. On the one hand, some FTP concepts are proposed for the spatially and temporally system with different dimensions of output and input. By choosing appropriate Lyapunov functionals and controllers, several sufficient conditions are presented to ensure the FTP of these CRDNNs. On the other hand, the FTS problem is also discussed for the multiple weighted CRDNNs with and without coupling delays, respectively.

March 2023

Jin-Liang Wang

Tiangong University

Shun-Yan Ren

University of Electronic Science and Technology of China

Huai-Ning Wu

Beihang University

Tingwen Huang

Shenzhen University of Advanced Technology

Acknowledgments

This book was supported in part by the National Natural Science Foundation of China under Grants 62173244, 62073011, and 92271115; and the Tianjin Natural Science Foundation for Distinguished Young Scholars under Grant 23JCJQJC00010. I begin by acknowledging my postgraduates Xiao Han, Wanlu Wei, and Kun Ling who have unselfishly given their valuable time in arranging these raw materials into something I am proud of.

1Synchronization for Complex Networks with Multiple Weights Under Recoverable Attacks

1.1 Introduction

During the last decade, the dynamical behavior of complex networks (CNs) has aroused increasing attention because CNs prevalently exist in the real world. Particularly, synchronization has been an appealing research topic in CNs, and many meaningful results have been obtained [1–16]. By choosing appropriate adaptive state-feedback controllers and Lyapunov functional, Zhou et al. [1] discussed the global and local synchronization in a CN with uncertain coupling functions. In [4], the synchronization problem for a CN with switching disconnected topology was addressed, and some synchronization conditions were established for such a network model. Lv et al. [5] tackled the exponential synchronization problem for CNs with coupling delay based on the impulsive control and event-triggered control techniques. In [11], the synchronization problem for stochastic CNs was discussed via pinning control technique and graph theory, in which the topology structure may be unknown. Zhu et al. [14] used the adaptive control method to deal with the synchronization problem for a type of CNs with time-varying delay, in which the restriction that time delay is differentiable is removed.

For some practical networks, such as urban population flow networks, food webs, etc., may be better described by CNs with multiple weights (CNMWs). More recently, some authors have addressed the problem of synchronization for CNMWs [17–26]. Wang et al. [17] not only investigated the pinning synchronization in the CNMWs with undirected and directed topologies but also presented several feedback gains and coupling strengths adjustment schemes. In [18], a criterion of synchronization for output-strictly passive CNMWs was obtained, and the synchronization problem of CNMWs was further discussed based on the nodes- and edges-based pinning control approaches, and the output-strict passivity. Zhao et al. [23] introduced a multiple delayed CN model with uncertain inner coupling matrices and developed a criterion of synchronization through the adaptive control scheme for such a network model. Dong et al. [24] took into account the exponential synchronization of multiple delayed CNs with switching and fixed topologies by employing the scramblingness property for adjacency matrix. Qin et al. [26] analyzed the robust synchronization of multiple delayed CNs, and a criterion for guaranteeing the robust synchronization was also developed by employing the adaptive state-feedback controller.

It is well known that the network topology may be destroyed owing to the various forms of attacks (e.g., power grids, military communication networks, and so on [27–29]), which might lead to undesirable dynamical behavior in the CNs. Consequently, it is very meaningful to study the dynamical behavior for CNs under attacks. Recently, some researchers have studied the synchronization problem of CNs suffering the attacks [30,31]. Wang et al. [30] investigated the synchronization for multiple memristive neural networks with the communication links subject to attacks and developed several synchronization criteria based on inequality techniques, -matrix properties, and event-triggered control method. Wang et al. [31] gave a global synchronization criterion for a network model suffering the successful but recoverable attacks by exploiting the switching system theory and derived the upper bounds of the average recovering time and the attack frequency. Regretfully, the network models with single coupling were discussed in these existing works about the synchronization for CNs under attacks [30,31], and the synchronization for CNMWs subject to attacks has not yet been explored. Obviously, it is very valuable and significative to further address the synchronization problem of CNMWs suffering the attacks.

This chapter discusses the synchronization for CNs with multiple state couplings (CNMSCs) or CNs multiple delayed state couplings (CNMDSCs) under recoverable attacks, respectively. The main contributions of our work are summarized as follows. First, we not only give a sufficient condition for ensuring the synchronization of directed CNMSCs suffering the attacks but also further study the synchronization problem by selecting the suitable state-feedback controller. Second, the analysis and control for the synchronization problem of undirected CNMSCs subject to attacks are also discussed, and several synchronization criteria are presented based on some inequality techniques. Third, we not only develop several synchronization criteria for CNMDSCs under attacks by constructing appropriate Lyapunov functional but also devise the suitable state-feedback controller to ensure the network synchronization.

1.2 Preliminaries

1.2.1 Notations

Let ; for any real square matrix , ; and denote the smallest and the largest eigenvalues of real symmetric matrix.

1.2.2 Lemmas

Lemma 1.1 (See [32]) Define