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

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.