Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper is concerned with the state estimation problem for a class of discrete-time stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones. One example is given to illustrate the effectiveness of the proposed result.