State estimation of static neural networks with mixed delays: A new parameterized delay interval approach

Abstract

This work concerns with the issue of H∞ state estimation for static neural networks with both leakage delay and time-varying delay. First, a new parameterized delay interval approach is proposed, where two parameters are introduced to divide the delay interval into four segments. Second, an appropriate Lyapunov–Krasovskii functional (LKF) including non-integral quadratic delay-product-type functional and augmented terms is constructed by combining four delay intervals. Meanwhile, for the sake of cooperating with the constructed LKF effectively, generalized free-weighting-matrix integral inequality (GFWMII) is utilized to derive much less conservative criteria. Finally, a novel criterion is derived which ensures that the considered system is asymptotically stable with prescribed H∞ performance. The effectiveness of the achieved approach is demonstrated by three commonly used numerical examples.