Complex-Valued Neural Networks Systems with Time Delay

Stability Analysis and (Anti-)Synchronization Control
1. Auflage 2022
ISBN: 978-981-19-5449-8
Verlag: Springer

Buch, Englisch, 229 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 535 g

Reihe: Intelligent Control and Learning Systems

This book provides up-to-date developments in the stability analysis and (anti-)synchronization control area for complex-valued neural networks systems with time delay. It brings out the characteristic systematism in them and points out further insight to solve relevant problems. It presents a comprehensive, up-to-date, and detailed treatment of dynamical behaviors including stability analysis and (anti-)synchronization control. The materials included in the book are mainly based on the recent research work carried on by the authors in this domain.
The book is a useful reference for all those from senior undergraduates, graduate students, to senior researchers interested in or working with control theory, applied mathematics, system analysis and integration, automation, nonlinear science, computer and other related fields, especially those relevant scientific and technical workers in the research of complex-valued neural network systems, dynamic systems, and intelligent control theory.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Ziye

Wang, Zhen

Chen, Jian

Lin, Chong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik

Inhaltsverzeichnis

Contents1. Introduction1.1 Research Significance of Complex-Valued Neural Networks Systems1.2 History of Complex-Valued Neural Networks Systems1.3 Book Organization2. Stability Analysis of Delayed Complex-Valued Neural Networks Systems2.1 Introduction2.2 Problem Formulation2.3 Stability Analysis Based on Separable Method2.4 Further Stability Analysis Based on Separable Method2.5 Stability Analysis Based on Nonseparable Method2.6 Illustrative Examples2.7 Conclusion and Notes3. Further Behavior Analysis about Stability and Hopf Bifurcation3.1 Introduction3.2 Problem Formulation3.3 Stability Result3.4 Hopf Bifurcation Results3.5 Illustrative Examples3.6 Conclusion4. Stability Analysis Based on Nonlinear Measure Approach 4.1 Introduction4.2 Problem Formulation4.3 Sufficient Condition to Ensure the Existence and Uniqueness of the Equilibrium Point4.4 Finite-Time Stability Result4.5 Illustrative Examples4.6 Conclusion5. Lagrange Exponential Stability for Delayed Complex-Valued Neural Networks Systems 5.1 Introduction5.2 Problem Formulation5.3 Sufficient Criteria Based on Algebraic Structure5.4 Sufficient Condition in Terms of LMI5.5 Illustrative Examples5.6 Conclusion6. Synchronization Control: Nonseparable Case6.1 Introduction6.2 Problem Formulation6.3 Synchronization Result for Delayed Complex-Valued Inertial Neural Networks 6.4 Illustrative Example6.5 Conclusion7. Anti-Synchronization Control: Nonseparable Case7.1 Introduction7.2 Problem Formulation7.3 Anti-Synchronization Result for Delayed Complex-Valued Inertial Neural Networks7.4 Anti-Synchronization Result for Delayed Complex-Valued Neural Networks7.5 Illustrative Examples7.6 Conclusion8. Anti-Synchronization Control: Separable Case 8.1 Introduction8.2 Problem Formulation8.3 Anti-Synchronization Result for Delayed Complex-Valued Neural Networks 8.4 Anti-Synchronization Result for Delayed Complex-Valued Bidirectional Associative Memory Neural Networks8.5 Illustrative Examples8.6 Conclusion9. Finite/Fixed-Time Synchronization Control9.1 Introduction9.2 Problem Formulation9.3 Finite-Time Synchronization Result9.4 Fixed-Time Synchronization Result9.5 Illustrative Examples10. Fixed-Time Pinning Synchronization and Adaptive Synchronization10.1 Introduction10.2 Problem Formulation10.3 Results for Delayed Complex-Valued Inertial Neural Networks 10.4 Results for Delayed Complex-Valued BAM Neural Networks10.5 Illustrative Examples10.6 ConclusionReferencesIndex

Über Autor(innen)

Ziye Zhang received the B.Sc. degree in mathematics from Yantai University, Yantai, China, in 2002, the M.Sc. degree in mathematics from Lanzhou University, Lanzhou, China, in 2005, and the Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2015. She is currently Associate Professor with the College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China. Her current research interests include systems analysis, fuzzy control, filter design, and neural networks.
Zhen Wang is currently Professor at College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China. He received Ph.D. degree from School of Automation, Nanjing University of Science and Technology, China, in 2013.
Jian Chen is Associate Professor at School of information and Control Engineering, Qingdao University of Technology, Qingdao, China. She received her Ph.D. degree from Institute of Complexity Science, Qingdao University, in 2017. Her research interest includes systems analysis and control.
Chong Lin is Professor at Institute of Complexity Science, Qingdao University, China. He received Ph.D. from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 1999.