Exponential æ_∞ stable learning method for Takagi-Sugeno fuzzy delayed neural networks: A convex optimization approach

摘要

In this paper, we propose some new results on stability for Takagi-Sugeno fuzzy delayed neural networks with a stable learning method. Based on the Lyapunov-Krasovskii approach, for the first time, a new learning method is presented to not only guarantee the exponential stability of Takagi-Sugeno fuzzy neural networks with time-delay, but also reduce the effect of external disturbance to a prescribed attenuation level. The proposed learning method can be obtained by solving a convex optimization problem which is represented in terms of a set of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed learning method.