Matrix analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Differential Inclusions: Set-Valued Maps and Viability Theory
Differential Inclusions: Set-Valued Maps and Viability Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Motif discoveries in unaligned molecular sequences using self-organizing neural networks
IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks
IEEE Transactions on Neural Networks
Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
Robust H∞ filter design of delayed neural networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
State estimation of markovian jump neural networks with mixed time delays
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Quasi-synchronization of delayed coupled networks with non-identical discontinuous nodes
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Discontinuous dynamical systems, particularly neural networks with discontinuous activation functions, arise in a number of applications and have received considerable research attention in recent years. In this paper, the robust state estimation problem is investigated for uncertain neural networks with discontinuous activations and time-varying delays, where the neuron-dependent nonlinear disturbance on the network outputs are only assumed to satisfy the local Lipschitz condition. Based on the theory of differential inclusions and nonsmooth analysis, several criteria are presented to guarantee the existence of the desired robust state estimator for the discontinuous neural networks. It is shown that the design of the state estimator for such networks can be achieved by solving some linear matrix inequalities, which are dependent on the size of the time derivative of the time-varying delays. Finally, numerical examples are given to illustrate the theoretical results.