Two-Objective Filtering for Takagi-Sugeno Fuzzy Hopfield Neural Networks with Time-Variant Delay

This paper focuses on the issue of two-objec-tive filtering for Takagi-Sugeno fuzzy Hopfield neural networks with time-variant delay. The intention is to design a fuzzy filter subject to random occurring gain perturbations to make sure that the filtering-error system achieves a pre-defined H-infinity and L-2-L-infinity disturbance attenuation level in mean square simultaneously. Without imposing any additional constraints on the differentiability of the time-delay function, a criterion of the mean-square H-infinity and L-2-L-infinity performance analysis for the filtering-error system is derived by means of an augmented Lyapunov functional and the second-order Bessel-Legendre inequality. Then, a numerically tractable design scheme is developed for the desired non-fragile H-infinity and L-2-L-infinity filter, where the gains are able to be determined by the solution of some linear matrix inequalities. At last, a numerical example with simulations is provided to illustrate the applicability and superiority of the present H-infinity and L-2-L-infinity filtering method.