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| Discontinuous dynamical systems, especially 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 delay, 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 two linear matrix inequalities (LMIs), which are dependent on the size of the time derivative of the time-varying delay. Finally, numerical examples are given to illustrate the theoretical results. |
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| Keywords:State estimation;Discontinuous activation functions; Filippov solution; Differential inclusions;Nonsmooth analysis;LMI approach |
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