Stability Analysis of Fuzzy Hopfield Neural Networks with Timevarying Delays

Abstract

School of Information Engineering, Yancheng Teachers University, 224002 Yancheng, China \u00a0 (Received June 07 2018, accepted August 22 2018) In this paper, the problem of asymptotic stability for Takagi-Sugeno (T-S) fuzzy Hopfield neural networks with \u00a0time-varying \u00a0delays \u00a0is \u00a0studied. \u00a0Based \u00a0on \u00a0the \u00a0Lyapunov \u00a0functional \u00a0method, \u00a0considering \u00a0the \u00a0system \u00a0with uncertainties or without uncertainties, new delay-dependent stability criteria are derived in terms of Linear Matrix Inequalities (LMIs) that can be calculated easily by the LMI Toolbox in MATLAB. The proposed approach does not involve free weighting matrices and can provide less conservative results than some existing ones. Besides, numerical examples are given to show the effectiveness of the proposed approach.