H∞ state estimation of static neural networks with time-varying delay

Abstract

The problem of H∞ state estimation is investigated for static neural networks with time-varying delays. Both delay-dependent and delay-independent criteria are presented such that the resulting error system is globally asymptotically stable with a guaranteed H∞ performance. The desired estimator matrix gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Numerical examples are addressed to show the effectiveness of the proposed design methods.