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

  • Xuechao Yan
  • Dongbing TongEmail author
  • Qiaoyu ChenEmail author
  • Wuneng Zhou
  • Yuhua Xu


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.


State estimation Neural networks Fractional Brownian motion (FBM) Asymptotic stability Exponential stability 



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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic and Electrical EngineeringShanghai University of Engineering ScienceShanghaiChina
  2. 2.School of Statistics and MathematicsShanghai Lixin University of Accounting and FinanceShanghaiChina
  3. 3.College of Information Science and TechnologyDonghua UniversityShanghaiChina
  4. 4.School of FinanceNanjing Audit UniversityJiangsuChina

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