Yantao Wang – författare

This book offers an essential introduction to the latest advances in delayed genetic regulatory networks (GRNs) and presents cutting-edge work on the analysis and design of delayed GRNs in which the system parameters are subject to uncertain, stochastic and/or parameter-varying changes. Specifically, the types examined include delayed switching GRNs, delayed stochastic GRNs, delayed reaction–diffusion GRNs, delayed discrete-time GRNs, etc. In addition, the solvability of stability analysis, control and estimation problems involving delayed GRNs are addressed in terms of linear matrix inequality or M-matrix tests.

The book offers a comprehensive reference guide for researchers and practitioners working in system sciences and applied mathematics, and a valuable source of information for senior undergraduates and graduates in these areas. Further, it addresses a gap in the literature by providing a unified and concise framework for the analysis and design of delayed GRNs.

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.