State estimation for static neural networks with time-varying delay

Authors:
He Huang;Gang Feng;Jinde Cao
Affiliations:
School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China and Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, ...;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China;Department of Mathematics, Southeast University, Nanjing 210096, PR China
Venue:
Neural Networks
Year:
2010

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Abstract

This paper is concerned with the state estimation problem for a class of static neural networks with time-varying delay. Here the time derivative of the time-varying delay is no longer required to be smaller than one. A delay partition approach is proposed to derive a delay-dependent condition under which the resulting error system is globally asymptotically stable. The design of a desired state estimator for such kinds of delayed neural networks can be accomplished by means of solving a linear matrix inequality. A simulation example is finally given to show the application of the developed approach.