A scaling parameter approach to delay-dependent state estimation of delayed neural networks

Authors:
He Huang;Gang Feng
Affiliations:
Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong and School of Electronic Information, Soochow University, Suzhou, China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2010

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Abstract

This brief is concerned with studying the delay-dependent state estimation problem of recurrent neural networks with time-varying delay. The neuron activation function is more general than the sigmoid functions, and the time-varying delay is allowed to vary fast with time. A scaling parameter based approach is proposed, and a delay-dependent criterion is derived under which the resulting error system is globally asymptotically stable. It is shown that the design of a proper state estimator is directly accomplished by means of the feasibility of a linear matrix inequality. Thanks to the introduction of a scaling parameter, the developed result can efficiently be applied to chaotic delayed neural networks.