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

被引：0
|
作者：
Xuechao Yan
Dongbing Tong
Qiaoyu Chen
Wuneng Zhou
Yuhua Xu
机构：
[1] Shanghai University of Engineering Science,School of Electronic and Electrical Engineering
[2] Shanghai Lixin University of Accounting and Finance,School of Statistics and Mathematics
[3] Donghua University,College of Information Science and Technology
[4] Nanjing Audit University,School of Finance
来源：
Neural Processing Letters | 2019年 / 50卷
关键词：
State estimation; Neural networks; Fractional Brownian motion (FBM); Asymptotic stability; Exponential stability;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：2007 / 2020
页数：13
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