An LMI approach to delay-dependent state estimation for delayed neural networks

Authors:
He Huang;Gang Feng;Jinde Cao
Affiliations:
Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, PR China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, PR China;Department of Mathematics, Southeast University, Nanjing 210096, PR China
Venue:
Neurocomputing
Year:
2008

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Abstract

This paper is concerned with the state estimation problem for a class of neural networks with time-varying delay. Comparing with some existing results in the literature, the restriction such as the time-varying delay was required to be differentiable or even its time-derivative was assumed to be smaller than one, are removed. Instead, the time-varying delay is only assumed to be bounded. A delay-dependent condition is developed to estimate the neuron states through observed output measurements such that the error-state system is globally asymptotically stable. The criterion is formulated in terms of linear matrix inequality (LMI), which can be checked readily by using some standard numerical packages. An example with simulation results is given to illustrate the effectiveness of the proposed result and the improvement over the existing ones.