Optimal chaotic synchronization of stochastic delayed recurrent neural networks

Ziqian Liu
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Abstract

This paper presents a theoretical design of how an optimal synchronization is achieved for stochastic delayed recurrent neural networks. According to the concept of drive-response, a control method is developed to guarantee that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals. The formulation of a nonlinear optimal control law is rigorously derived by using Lyapunov technique and solving a Hamilton-Jacobi-Bellman (HJB) equation. To verify the analytical results, a numerical example is given to demonstrate the effectiveness of the proposed approach, which is simple and easy to implement in reality.
随机延迟递归神经网络的最优混沌同步
本文提出了一种实现随机延迟递归神经网络最优同步的理论设计。根据驱动-响应的概念,提出了一种控制方法,以保证混沌驱动网络与受不确定噪声信号影响的混沌响应网络同步。利用李雅普诺夫技术,通过求解Hamilton-Jacobi-Bellman (HJB)方程,严格推导出非线性最优控制律的表达式。为了验证分析结果,给出了一个数值算例,证明了该方法的有效性,该方法简单,易于在实际中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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