Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (16): 60-62.

• 理论研究 • Previous Articles Next Articles

Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays

WANG Yong¹,JIANG Zhen¹,CHENG Si-wei²

1.College of Computer and Communication Engineering，Changsha University of Science and Technology，Changsha 410076，China
2.College of Mathematics and Calculation Science，Changsha University of Science and Technology，Changsha 410076，China

Received:2007-09-11 Revised:2007-12-28 Online:2008-06-01 Published:2008-06-01
Contact: WANG Yong

时滞Hopfield神经网络的随机稳定性分析

王勇¹,蒋真¹,程思蔚²

1.长沙理工大学计算机与通信工程学院，长沙 410076
2.长沙理工大学数学与计算科学学院，长沙 410076

通讯作者: 王勇

Abstract

Abstract: The ordinary Takagi Sugeno（T-S） fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning.In this paper，stochastic fuzzy Hopfield neural networks with time-varying delays（SFVDHNNs） are studied.The model of SFVDHNN is first established，then，the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach.Stability criterion is derived in terms of Linear Matrix Inequalities（LMIs），which can be effectively solved by some standard numerical packages.

Key words: stochastic stability, Hopfield neural network, exponential stability, time-varying system

摘要： T-S模型提供了一种通过模糊集和模糊推理将复杂的非线性系统表示为线性子模型的方法。研究了时滞Hopfield神经网络的随机稳定性（SFVDHNNs）。首先描述了SFVDHNNs模型，然后用Lyapunov方法研究了SFVDHNNs全局均方指数稳定性，通过可以被一些标准的数值分析方法求解的线性矩阵不等式（LMIs）得出了稳定性标准。

关键词: 随机稳定性, Hopfield神经网络, 均方指数稳定, 时变系统

WANG Yong¹,JIANG Zhen¹,CHENG Si-wei². Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays[J]. Computer Engineering and Applications, 2008, 44(16): 60-62.

王勇¹,蒋真¹,程思蔚². 时滞Hopfield神经网络的随机稳定性分析[J]. 计算机工程与应用, 2008, 44(16): 60-62.

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Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays

时滞Hopfield神经网络的随机稳定性分析

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