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Exponential Stabilization of Memristive Neural Networks With Time Delays

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2 Author(s)
Ailong Wu ; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China ; Zhigang Zeng

In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks. In addition, a simplified and effective algorithm is considered for design of the optimal controller. These conditions are the improvement and extension of the existing results in the literature. Two numerical examples are given to illustrate the theoretical results via computer simulations.

Published in:

IEEE Transactions on Neural Networks and Learning Systems  (Volume:23 ,  Issue: 12 )