An improved stability result for delayed Takagi–Sugeno fuzzy Cohen–Grossberg neural networks

Abstract

This work proposes a novel and improved delay independent global asymptotic stability criterion for delayed Takagi–Sugeno (T–S) fuzzy Cohen–Grossberg neural networks exploiting a suitable fuzzy-type Lyapunov functional in the presence of the nondecreasing activation functions having bounded slopes. The proposed stability criterion can be easily validated as it is completely expressed in terms of the system matrices of the fuzzy neural network model considered. It will be shown that the stability criterion obtained in this work for this type of fuzzy neural networks improves and generalizes some of the previously published stability results. A constructive numerical example is also given to support the proposed theoretical results.