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Resilient Filtering for Delayed Markov Jump Neural Networks via Event-triggered Strategy

Weifeng Xia*, Yongmin Li, Zuxin Li, Shuxin Du, Bo Li, and Wenbin Chen
International Journal of Control, Automation, and Systems, vol. 19, no. 10, pp.3332-3342, 2021

Abstract : This paper deals with the event triggered filtering problem for a class of delayed discrete-time Markov jump neural networks, where a resilient filter with parameter uncertainties is adopted. The aim of this paper is to design a suitable filter which ensures that the filtering error system is stochastically stable and satisfies a prescribed mixed passivity and H∞ performance. Sufficient conditions for solvability of such a problem are developed. Based on the obtained conditions, an explicit expression of the desired resilient filter is proposed. Finally, an example is presented to show the usefulness of the proposed scheme.

Keyword : Event-triggered scheme, filtering, Markov chain, neural networks, time delay.

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