Global Exponential Stability of Takagi-Sugeno Fuzzy Cohen-Grossberg Neural Network with Time-Varying Delays

Abstract

In this letter, global exponential stability of Takagi-Sugeno fuzzy Cohen-Grossberg Neural Network (CGNN) with time-varying delay factor has been investigated based on the criteria of non-singular M-matrix and the Lyapunov stability technique. The stability inequality is derived with the help of Lipschitz condition for the nonlinear activation functions and a sufficient condition is shown to verify the criterion of the exponential stability condition for the CGNN with time-varying delay terms, which is described in the presence of delay terms of T-S Fuzzy model. Thus, the global exponential stability for T-S fuzzy CGNN in the presence of time-varying delay terms is derived in an easy way. This letter contains quite a new result for delayed CGNN for the T-S Fuzzy model. Finally, a numerical example is taken to validate the efficiency and unwavering quality, and to exhibit the superiority of the considered method as compared to the existing method for particular cases. © 2017 IEEE.