State estimation for discrete-time neural networks with time-varying delay

Abstract

This article deals with the problem of delay-dependent state estimation for discrete-time neural networks with time-varying delay. Our objective is to design a state estimator for the neuron states through available output measurements such that the error state system is guaranteed to be globally exponentially stable. Based on the linear matrix inequality approach, a delay-dependent condition is developed for the existence of the desired state estimator via a novel Lyapunov functional. The obtained condition has less conservativeness than the existing ones, which is demonstrated by a numerical example.

Keywords:

• neural networks
• time-varying delay
• state estimation
• exponential stability
• linear matrix inequality (LMI)

Acknowledgements

This work is supported by the National Creative Research Groups Science Foundation of China under Grant 60721062, the National Natural Science Foundation of PR China under Grants 60736021, 60804011, 60904001, the National High Technology Research and Development Programme of China under Grant 863 Programme 2008AA042902 and the Engineering and Physical Sciences Research Council, UK (EP/F029195).