Unified dissipativity state estimation for delayed generalized impulsive neural networks with leakage delay effects

Abstract

This paper examined for the first time the challenge of unified dissipativity state estimation of delayed generalized impulsive neural networks (GINNs) with leaky delay effects. To estimate the upper bounds of the Lyapunov Krasovskii functionals (LKFs), the tighter integral inequality (TII) and reciprocally convex inequality (RCI) approaches are employed, and certain criteria are provided in the form of linear matrix inequalities (LMIs). As a response, a new delayed-dependent criterion for establishing whether the estimated error network is unified dissipative is developed. By modifying the values of the suitable matrices, the notion of a unified dissipativity-based estimating state may be used to find the multi-dynamic state estimation of GINNs. The recommended technique’s benefit is displayed by multiple numerical examples, one of which was sponsored by a practical implementation of the benchmark problem (i.e., the quadruple-tank process system (QTPS)) that is involved with reasonable issues, particularly the impulse impacts in the sense of a unified dissipativity performance.