Design of nonlinear optimal control for chaotic synchronization of coupled stochastic neural networks via Hamilton-Jacobi-Bellman equation

被引：17

作者：

Liu, Ziqian ^{[1
]}

机构：

[1] SUNY Maritime Coll, Dept Engn, 6 Pennyfield Ave, Throggs Neck, NY 10465 USA

来源：

NEURAL NETWORKS | 2018年 / 99卷

关键词：

Chaotic synchronization; Nonlinear optimal control; Noise attenuation; Hamilton-Jacobi-Bellman (HJB) equation; Lyapunov technique; Coupled stochastic neural networks; EXPONENTIAL LAG SYNCHRONIZATION; SLIDING MODE CONTROL; TIME-VARYING DELAYS; UNKNOWN-PARAMETERS; FEEDBACK-CONTROL; SYSTEMS; CRITERIA;

D O I：

10.1016/j.neunet.2018.01.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new theoretical design of nonlinear optimal control on achieving chaotic synchronization for coupled stochastic neural networks. To obtain an optimal control law, the proposed approach is developed rigorously by using Hamilton-Jacobi-Bellman (HJB) equation, Lyapunov technique, and inverse optimality, and hence guarantees that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals. Furthermore, the paper provides four numerical examples to demonstrate the effectiveness of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：166 / 177

页数：12

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