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.

“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)

• Introduction to Neural Networks.- Preliminaries on Dynamical Systems and Stability Theory.- Survey of Dynamics of Cohen-Grossberg Type RNNs.- Delay-partitioning-method Based Stability Result for RNNs.- Stability result on the static and vector field recurrent neural networks.- Stability Criteria for RNNs Based on Secondary Delay Partitioning.- LMI-based Stability Criteria for Static Neural Networks.- Multiple Stability for Discontinuous RNNs.- LMI-based Passibity Criteria for RNNs with Delays.- Dissipativity and Invariant Sets for Neural Networks with Delay.- Synchronization Stability in Complex Neural Networks.- Stabilization of Stochastic RNNs with Stochastic Delays.- Adaptive Synchronization of Complex Neural Networks

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.

“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)

• Introduction to Neural Networks.- Preliminaries on Dynamical Systems and Stability Theory.- Survey of Dynamics of Cohen-Grossberg Type RNNs.- Delay-partitioning-method Based Stability Result for RNNs.- Stability result on the static and vector field recurrent neural networks.- Stability Criteria for RNNs Based on Secondary Delay Partitioning.- LMI-based Stability Criteria for Static Neural Networks.- Multiple Stability for Discontinuous RNNs.- LMI-based Passibity Criteria for RNNs with Delays.- Dissipativity and Invariant Sets for Neural Networks with Delay.- Synchronization Stability in Complex Neural Networks.- Stabilization of Stochastic RNNs with Stochastic Delays.- Adaptive Synchronization of Complex Neural Networks

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.

“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)

• Introduction to Neural Networks.- Preliminaries on Dynamical Systems and Stability Theory.- Survey of Dynamics of Cohen-Grossberg Type RNNs.- Delay-partitioning-method Based Stability Result for RNNs.- Stability result on the static and vector field recurrent neural networks.- Stability Criteria for RNNs Based on Secondary Delay Partitioning.- LMI-based Stability Criteria for Static Neural Networks.- Multiple Stability for Discontinuous RNNs.- LMI-based Passibity Criteria for RNNs with Delays.- Dissipativity and Invariant Sets for Neural Networks with Delay.- Synchronization Stability in Complex Neural Networks.- Stabilization of Stochastic RNNs with Stochastic Delays.- Adaptive Synchronization of Complex Neural Networks