This book provides a direct method based on system solutions to address the problems related to the analysis and control of delayed neural networks. The method proposed in this book is important for the following reasons: It does not involve the construct of any Lyapunov-Krasovskii functional (LKF), which overcomes the difficulty in constructing an appropriate and effective LKF; It can provide more simpler sufficient conditions, and hence it possesses less computational complexity; It can result in delay-dependent global exponential stability criteria that can used to give the decay rate estimation of the state; It is suitable for analysis and design problems of most system models with (multiple) delays after a small modification. The book is divided into 11 chapters, and focuses on the analysis and design problems related to delayed neural networks. It is written for graduate students and research level mathematicians and is suitable for postgraduates or as a reference.

Overcomes the difficulty in finding suitable LKF since it does not involve the construct of any LKF Provides more simpler sufficient conditions, with less computational complexity Suitable for analysis and design problems of most system models with (multiple) delays after a small modification

Auteur

Xin Wang, Yu Xue, Xian Zhang and Yantao Wang are with the School of Mathematical Science, Heilongjiang University, Harbin 150080, China, and also with the Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Heilongjiang University, Harbin 150080, China.

Xin Wang received the Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China, in 2016. His research interests include fault diagnosis and fault-tolerant control, and time-delay systems. Yu Xue received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2007. Her research interests include the analysis and design of delayed dynamic systems. Xian Zhang received the Ph.D. degree in control theory from the Queen's University of Belfast, Belfast, U.K., in 2004. His research interests include neural networks and genetic regulatory networks. Yantao Wang has been dedicated to the research of neural networks since she started working.

Contenu

Introduction Backgrounds of NNs.- Global Exponential Stability and Stabilization.- Global Exponential Stability Affected Impulses.- Global Exponential Synchronization.- State Estimation.- Global Exponential Stability and Stabilization.- Lagrangian Global Exponential Stability and Stabilization.- Global Robust Exponential Stability.- H Control.- Global Exponential Synchronization.- Lp Stability.