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Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay

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3 Author(s)
He Huang ; Dept. of Manuf. Eng. & Eng. Manage., City Univ. of Hong Kong, Hong Kong ; Gang Feng ; Jinde Cao

The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of the time-varying delay and on the size of the time derivative of the time-varying delay. It is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. Finally, two simulation examples are given to demonstrate the effectiveness of the developed approach.

Published in:

IEEE Transactions on Neural Networks  (Volume:19 ,  Issue: 8 )