Generalized Dissipativity State Estimation of Delayed Static Neural Networks Based on a Proportional-Integral Estimator With Exponential Gain Term
Guoqiang Tan, Zhanshan Wang
Abstract
This brief investigates the problem of generalized dissipativity state estimation for static neural networks (SNNs) with time-varying delay. Firstly, a proportional-integral (PI) estimator with exponential gain term is proposed, which unifies the Luenberger estimator and the Arcak estimator based on generalized dissipativity. Secondly, an improved reciprocally convex inequality is proposed, which can be used to tackle the derivative of the Lyapunov functional. As a result, a new generalized dissipativity state estimation criterion can be derived and the gains of the designed estimator can be obtained. Finally, simulation results are provided to demonstrate the advantage and the effectiveness of the proposed method over the existing ones.