Equilibrium and Stability Analysis of Takagi-Sugeno Fuzzy Delayed Cohen-Grossberg Neural Networks
This paper carries out an investigation into the problem of the global asymptotic stability of the class of Takagi-Sugeno (T-S) fuzzy delayed Cohen-Grossberg neural networks with discrete time delays. A new sufficient criterion for the uniqueness and global asymptotic stability of the equilibrium point for this class of fuzzy neural networks is proposed. The uniqueness of the equilibrium point is proved by using the contradiction method. The stability of the equilibrium point is established by employing a new fuzzy type Lyapunov functional. The obtained stability result is obtained with respect to the nondecreasing and slope-bounded activation functions it can be shown to be independent of time delays. The proposed result can be easily verified by using some commonly used norm properties of matrices.