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wang x analysis design delayed neural networks 2025

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Textbook in PDF format 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. Artificial neural network (NN) is often called NN for short. The dynamics of NNs have been applied to multiple fields, such as associative memory, autonomous navigation, data encryption, intelligent control, medicine diagnosis, optimization, pattern recognition, and so forth. Roughly speaking, mathematical models of NNs are divided into the continuous-time ones and the discrete-time ones, which are described by differential equations and difference equations, respectively. Delays resulting from the communication mode among neurons are classified as transmission delay, leakage delay, and distributed delay. Since delay is inevitable, it is necessary to model NNs by functional differential/difference equations, that is, differential/difference equations with delayed states. As a result, many experts and scholars have paid attention to the study of delayed NNs, and many excellent results have been reported in literature. From the point of control theory, the research on delayed NNs includes mainly two aspects: analysis and design. The current studies mainly focus on the continuous-time case. However, digital computers process only discrete data, so discrete-time NNs can match compute performance better from the view of digitization than continuous ones. Thus, it is equally important to research the discrete-time NNs and the continuous-time ones. Introduction Part I Analysis and Design of Discrete-Time Delayed NNS Global Exponential Stability and Stabilization Global Exponential Stability Affected Impulses Global Exponential Synchronization State Estimation Part II Analysis and Design of Continuous-Time Delayed NNS Global Exponential Stability and Stabilization Lagrangian Global Exponential Stability and Stabilization Global Robust Exponential Stability H∞ Control Global Exponential Synchronization Lp Stability Appendix: Preliminaries

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wang x analysis design delayed neural networks 2025

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