Qualitative Analysis and Control of Complex Neural Networks with Delays

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction to Neural Networks

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 1-36

3. Preliminaries on Dynamical Systems and Stability Theory

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 37-90

4. Survey of Dynamics of Cohen–Grossberg-Type RNNs

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 91-172

5. Delay-Partitioning-Method Based Stability Results for RNNs

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 173-204

6. Stability Criteria for RNNs Based on Secondary Delay Partitioning

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 205-224

7. LMI-Based Stability Criteria for Static Neural Networks

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 225-237

8. Multiple Stability for Discontinuous RNNs

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 239-257

9. LMI-based Passivity Criteria for RNNs with Delays

   • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

   Pages 259-276

10. Dissipativity and Invariant Sets for Neural Networks with Delay

    • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

    Pages 277-309

11. Synchronization Stability in Complex Neural Networks

    • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

    Pages 311-331

12. Stabilization of Stochastic RNNs with Stochastic Delays

    • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

    Pages 333-360

13. Adaptive Synchronization of Complex Neural Networks

    • Zhanshan Wang, Zhenwei Liu, Chengde Zheng

    Pages 361-383

14. Back Matter

    Pages 385-388

    PDF

This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

• Complex Neural Network
• Consensus of MAS
• Dynamical Stability
• Multi-agent System
• Multistability of Neural Network
• Neural Network
• Optimal Computation
• Power System
• Synchronization Stability
• complexity

“Each chapter is independent and self-contained and the book can be read randomly according to one's requirements. … This book should benefit researchers and senior graduate/post-graduate students interested in the study of dynamical systems, by providing different kinds of mathematical tools and techniques. … this book will definitely benefit readers, providing new insight into the area of neural networks in particular and dynamical systems in general, thereby enriching the literature.” (Ponnado Raja Sekhara Rao, Mathematical Reviews, April, 2017)

• College of Information Science and Engineering, Northeastern University, Shenyang, China

  Zhanshan Wang

• College of Information Sci. and Eng., Northeastern University, Shenyang, China

  Zhenwei Liu

• Department of Mathematics, Dalian Jiaotong University, Dalian, China

  Chengde Zheng

Zhanshan Wang received B.S. degree in Electrical Automation from Inner Mongolia University of Science and Technology, Baotou, China in 1994, M.S. degree in Control Theory and Control Engineering from Liaoning Shihua University, Fushun in 2001 and Ph.D. degree in Control Theory and Control Engineering from Northeastern University, Shenyang in 2006, respectively. From 1994 to 1998 he was a technician in Fushun Plant, Liaoning. He is currently a Professor with the College of Information Science and Engineering, Northeastern University, Shenyang, China. His current research interests include stability analysis of dynamical system, neural networks, complex networks, multi-agent systems, computational intelligence, fault diagnosis, adaptive control and their applications.

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