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Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity | IEEE Journals & Magazine | IEEE Xplore

Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity


Abstract:

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The mod...Show More

Abstract:

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter σ, and asynchronous rate are revealed.
Published in: IEEE Transactions on Cybernetics ( Volume: 49, Issue: 7, July 2019)
Page(s): 2504 - 2513
Date of Publication: 20 April 2018

ISSN Information:

PubMed ID: 29993924

Funding Agency:


References

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