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On exponential stability results for fuzzy impulsive neural networks. (English) Zbl 1198.34160
Summary: Complex nonlinear systems can be represented as a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy neural networks with impulsive effect and time-varying delays is investigated. The model for fuzzy impulsive neural networks with time-varying delays is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of impulsive neural networks with time-varying delays. Secondly, the exponential stability for fuzzy impulsive neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) approach. In addition, two numerical examples are provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.
##### MSC:
 34K20 Stability theory of functional-differential equations 92B20 General theory of neural networks (mathematical biology) 34K36 Fuzzy functional-differential equations 34K45 Functional-differential equations with impulses
##### Software:
LMI Control Toolbox; Matlab; LMI toolbox
##### References:
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