H8 state estimation for T-S fuzzy reaction-diffusion delayed neural networks with randomly occurring gain uncertainties and semi-Markov jump parameters

This paper is concerned with the H-infinity state estimation issue for Takagi-Sugeno (T-S) fuzzy reaction-diffusion delayed neural networks (RDNNs) with randomly occurring gain uncertainties and semi-Markov jump parameters (sMJP). The considered gain perturbations are assumed to occur in a random manner and are modeled by a random variable with the Bernoulli distribution. Furthermore, different from the existing T-S fuzzy neural networks (NNs), as the first attempt, the reaction-diffusion phenomenon, the T-S fuzzy rules, and the sMJP are taken into account in the unified framework, which makes the proposed models more applicable. By utilizing the Lyapunov functional method and introducing a suitable free weight matrix, sufficient critered to guarantee the exponential stability and H(infinity & nbsp;)performance of estimation error. In order to improve the tolerance of the proposed estimator to gain variations, a fuzzy resilient estimator design scheme is presented with the aid of some decoupling techniques. Finally, two numerical simulations verify the effectiveness and superiority of the proposed scheme. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the H-infinity state estimation problem for Takagi-Sugeno fuzzy reaction-diffusion delayed neural networks (RDNNs) with randomly occurring gain uncertainties and semi-Markov jump parameters (sMJP). The proposed fuzzy resilient estimator design scheme considers the random gain perturbations and introduces a free weight matrix and decoupling techniques to improve the tolerance to gain variations. Two numerical simulations verify the effectiveness and superiority of the proposed scheme.