Abstract
This paper is concerned with the H∞ 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∞ 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.
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