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Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms

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Abstract

This paper investigates the problem of state estimation for Takagi–Sugeno (T–S) fuzzy Markov jump delayed neural networks with reaction–diffusion terms. A memory-based control scheme that contains a constant signal transmission delay is adopted, which is the first attempt to handle the issue of state estimation for fuzzy neural networks. Firstly, several conditions that guarantee the stability of the considered system are derived. Then, the fuzzy memory-based controller design scheme is proposed. Finally, three numerical examples are given to demonstrate the validity of the proposed method.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant U1604146, and by the Foundation for the University Technological Innovative Talents of Henan Province under Grant 18HASTIT019.

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Authors and Affiliations

  1. School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Xiaona Song, Jingtao Man, Zhumu Fu & Mi Wang

  2. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210042, China

    Junwei Lu

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  1. Xiaona Song
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  2. Jingtao Man
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Correspondence to Xiaona Song.

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Song, X., Man, J., Fu, Z. et al. Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms. Neural Process Lett 50, 2529–2546 (2019). https://doi.org/10.1007/s11063-019-10026-8

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