Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/3151
Title: State estimation for delayed neural networks
Authors: Wang, Z
Ho, DWC
Liu, X
Keywords: Exponential stability; Linear matrix inequalities (LMIs);Neural networks; State estimation; Time-delays
Issue Date: 2005
Publisher: IEEE
Citation: IEEE Transactions on Neural Networks, 16 (1): 279-284
Abstract: In this letter, the state estimation problem is studied for neural networks with time-varying delays. The interconnection matrix and the activation functions are assumed to be norm-bounded. The problem addressed is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally exponentially stable. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. In particular, we derive the conditions for the existence of the desired estimators for the delayed neural networks. We also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities (LMIs). Finally, it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks. Numerical examples are included to illustrate the applicability of the proposed design method.
Description: Copyright [2005] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://bura.brunel.ac.uk/handle/2438/3151
ISSN: 1045-9227
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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