Stability and Synchronization Control of Stochastic Neural Networks
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Beschreibung
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
Introduces for the first time the stochastic disturbance driven by Levy process in modeling stochastic neural networks Applies the M-matrix method to analyze and synthesize the synchronization criteria for stochastic neural networks Extends the existing results of the adaptive synchronization criteria of stochastic neural networks by getting the exponential stability (in the pth moment) conditions of the general stochastic delay deferential equation and the general neutral-type stochastic delay deferential equation, respectively Includes supplementary material: sn.pub/extras
Autor*in
Wuneng Zhou
Themen in »Stability and Synchronization Control of Stochastic Neural Networks«
Adaptive control M-matrix method Markovian switching Neural networks Stability Stochastic disturbance Synchronization
Stimmen zu »Stability and Synchronization Control of Stochastic Neural Networks«
“Neural networks are important tools for solving problems in many fields of applied sciences. … The volume is equipped with many figures, numerical examples and numerical simulations. Moreover, each chapter contains several references. The book can be recommended to readers having good knowledge in the foundations of neural networks, dynamical control systems and stochastic analysis.” (Kurt Marti, zbMATH 1355.60007, 2017)
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