Passive state estimation for fuzzy jumping neural networks with fading channels based on the hidden Markov model

Xuelian Wang, Jianwei Xia, Jing Wang, Jian Wang and Zhen Wang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 535, issue C

Abstract: The passive state estimation issue of the fuzzy Markov jump neural networks with fading channels is investigated in this paper. In view of the fact that it is difficult for the estimator to completely obtain the mode information of the neural networks in the actual situations, the hidden Markov model is utilized to depict the mode mismatching phenomenon between the networks and estimator. Moreover, aimed at the circumstance that data are transmitted over fading channels, and an improved discrete-time Rice fading model with the mode-dependent channel coefficients is employed. The principal goal is to devise a state estimator which can ensure that the error system realizes the stochastic stability and satisfies the passive performance index. By establishing a suitable mode-dependent Lyapunov–Krasovskii functional, sufficient conditions guaranteeing the realization of the designed state estimator are presented. The advantages and effectiveness of the adopted design scheme are verified by an illustrated example.

Keywords: Fuzzy Markov jump neural networks; Fading channels; Hidden Markov model; Passive state estimation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314025

DOI: 10.1016/j.physa.2019.122437

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