EQUILIBRIUM AND STABILITY ANALYSIS OF TAKAGI-SUGENO FUZZY DELAYED COHEN-GROSSBERG NEURAL NETWORKS

Abstract

This paper carries out an investigation into the problem of theglobal asymptotic stability of the class of Takagi-Sugeno (T-S) fuzzy delayedCohen-Grossberg neural networks involving discrete time delays and employingthe nondecreasing and slope-bounded activation functions. A new su¢ cientcriterion for the uniqueness and global asymptotic stability of the equilibriumpoint for this class of fuzzy neural networks is proposed. The uniqueness ofthe equilibrium point is proved by using the contradiction method, and thestability of the equilibrium point is established by utilizing a novel fuzzy typeLyapunov functional. The obtained stability condition is independent of thetime delay parameters and, it can be easily veriÖed by exploiting some commonly used norm properties of matrices. A constructive numerical example isalso given to demonstrate the applicability of the proposed stability condition.