【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。更多还原

【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.更多还原