Affiliations: College of Computer and Information, Hohai University, Changzhou, China
Correspondence:
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Corresponding author. Mingang Hua, College of Computer and Information, Hohai University, Changzhou, China. Tel.: +86 519 85192041; Fax: +86 519 85191838; E-mail: [email protected].
Note: [1] This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20130239) and the Research Fund for the Doctoral Program of Higher Education of China (No. 20130094120015)
Abstract: This paper investigates non-fragile exponential state estimation problems for continuous-time fuzzy stochastic neural networks with time-varying delays. The Takagi-Sugeno (T-S) fuzzy model representation is extended to the exponential state estimator design for fuzzy stochastic neural networks with time-varying delays. The neuron activation function and the nonlinear measurement equation are assumed to be satisfy sector-bounded conditions and standard Lipschitz conditions. For these two conditions, delay-dependent sufficient conditions are presented to guarantee the existence of the desired state estimators for fuzzy stochastic neural networks. Finally, two numerical examples are given to demonstrate that the proposed approaches are effective and that the sector-bounded conditions are weaker than the standard Lipschitz conditions.
Keywords: Non-fragile, state estimation, Takagi-Sugeno, stochastic neural networks, exponential stability
