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Stubborn state estimation for complex-valued neural networks with mixed time delays: the discrete time case

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Abstract

In this paper, the state estimation problem is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with mixed time delays. We consider a scenario that the measurement output may contain the unexpected outliers. In order to attenuate the impact of measurement outliers on the state estimation performance, a stubborn state estimator is designed for discrete-time CVNNs. For the purpose of analysis and synthesis, the CVNNs under consideration are firstly transformed to an augmented system which includes the dynamics of the real and imaginary parts of original CVNNs. Then, by resorting to the Lyapunov functional approach, a sufficient condition is given to ensure that the estimation error system is asymptotically stable. Subsequently, the desired state estimator gain is determined by solving a set of matrix inequalities. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed stubborn state estimation scheme.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61873059 and 61922024, the Program of Shanghai Academic/Technology Research Leader under Grant 20XD1420100, and the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University of China under Grants CUSF-DH-D-2020085 and CUSF-DH-D-2021056.

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Yufei Liu, Bo Shen & Jie Sun

  2. Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, 201620, China

    Yufei Liu, Bo Shen & Jie Sun

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  1. Yufei Liu
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  2. Bo Shen
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  3. Jie Sun
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Correspondence to Bo Shen.

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Liu, Y., Shen, B. & Sun, J. Stubborn state estimation for complex-valued neural networks with mixed time delays: the discrete time case. Neural Comput & Applic 34, 5449–5464 (2022). https://doi.org/10.1007/s00521-021-06707-y

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  • DOI: https://doi.org/10.1007/s00521-021-06707-y

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