Regular Papers
International Journal of Control, Automation and Systems 2021; 19(10): 3332-3342
Published online July 27, 2021
https://doi.org/10.1007/s12555-020-0678-0
© The International Journal of Control, Automation, and Systems
Resilient Filtering for Delayed Markov Jump Neural Networks via Event-triggered Strategy
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.
Keywords Event-triggered scheme, filtering, Markov chain, neural networks, time delay.
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Regular Papers
International Journal of Control, Automation and Systems 2021; 19(10): 3332-3342
Published online October 1, 2021 https://doi.org/10.1007/s12555-020-0678-0
Copyright © The International Journal of Control, Automation, and Systems.
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
Huzhou University
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.
Keywords: Event-triggered scheme, filtering, Markov chain, neural networks, time delay.
Vol. 23, No. 3, pp. 683~972
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