Robust state estimation for uncertain stochastic bidirectional associative memory networks with time-varying delays

In this paper, we investigate the problem of robust state estimation for uncertain stochastic bidirectional associative memory (BAM) networks with time-varying delays. BAM networks are quite general and include parameter uncertainties, time-varying delays and stochastic disturbances. The activation functions are allowed to satisfy sector-like nonlinearities. The main purpose of investigating this problem is to design a linear estimator to approximate the state of the networks through available measurement outputs. By using the Lyapunov functional method and some stochastic analysis techniques, sufficient conditions are established in terms of linear matrix inequalities (LMIs) which can be checked easily by the Matlab toolbox. It is also shown that the designed state estimator can guarantee the estimation error dynamics to be globally robustly asymptotically stable in the mean square. Two illustrated examples are given to demonstrate the feasibility of the proposed estimation schemes.

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