Extended dissipative state estimation for static neural networks via delay-product-type functional

Abstract

This paper studies the issue of extended dissipative state estimation for static neural networks with time-varying delays. A new delay-product-type (DPT) functional is constructed to introduce triple integrals, which can encompass some existing DPT functionals as its special cases, which leads to less conservative results. A parameter-dependent reciprocally convex inequality (PDRCI) covering some existing results is proposed to estimate the DPT functional, which can reach a tighter bound. Based on these ingredients, a novel estimator design condition is obtained to ensure the estimation error system to be asymptotically stable and extended dissipative. By using a matrix inequality decoupling technique, the estimator gain matrices can be solved by linear matrix inequalities (LMIs). Compared with some existing works, the restrictions on slack matrices are overcome, which increase the flexibility of estimator solutions. The effectiveness of the developed method is illustrated by an example.