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Adaptive State Estimation of Stochastic Delayed Neural Networks with Fractional Brownian Motion

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Abstract

This paper considers the adaptive state estimation problem for stochastic neural networks with fractional Brownian motion (FBM). The problem for the stochastic neural networks with FBM is handled according to the theory of Hilbert–Schmidt and the principle of analytic semigroup. Using the stochastic analytic technique and adaptive control method, the asymptotic stability and the exponential stability criteria are established. Finally, a simulation example is given to prove the efficiency of developed criteria.

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Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Xuechao Yan & Dongbing Tong

  2. School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, 201620, China

    Qiaoyu Chen

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

    Wuneng Zhou

  4. School of Finance, Nanjing Audit University, Jiangsu, 211815, China

    Yuhua Xu

Authors
  1. Xuechao Yan
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  2. Dongbing Tong
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  3. Qiaoyu Chen
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  4. Wuneng Zhou
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  5. Yuhua Xu
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Correspondence to Dongbing Tong or Qiaoyu Chen.

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This work is partially supported by the National Natural Science Foundation of China (61673257; 11501367; 61573095; 61673221); the Natural Science Foundation of Jiangsu Province (BK20181418); the fifteenth batch of Six Talent Peaks project in Jiangsu Province (DZXX-019).

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Yan, X., Tong, D., Chen, Q. et al. Adaptive State Estimation of Stochastic Delayed Neural Networks with Fractional Brownian Motion. Neural Process Lett 50, 2007–2020 (2019). https://doi.org/10.1007/s11063-018-9960-z

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