4.6 Article

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

期刊

IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 7, 页码 2504-2513

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2824853

关键词

Extended dissipativity; hidden Markov model; interval gain uncertainty; Markov jump neural networks

资金

  1. Science Fund for Creative Research Groups of the National Natural Science Foundation of China [61621002]
  2. China National Funds for Distinguished Young Scientists [61425009]
  3. National Natural Science Foundation of China [U1611262, 61603102]
  4. Fundamental Research Funds for the Central Universities [2017FZA5010]
  5. Science and Technology Planning Project of Guangdong Province [2017B010116006]
  6. Zhejiang Provincial Natural Science Foundation of China [LR16F030001]
  7. Guangdong Province

向作者/读者索取更多资源

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 sigma, and asynchronous rate are revealed.

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