Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach

Authors

1 Department of Mathematics, National Institute of Technology – Deemed University, Tiruchirappalli 620 015, Tamil Nadu, India

2 Department of Mathematics, Gandhigram Rural Institute – Deemed University, Gandhigram 624 302, Tamil Nadu, India

10.21608/joems.2016.386829

Abstract

In this paper, the sampled measurement is used to estimate the neuron states, instead of
the continuous measurement, and a sampled-data estimator is constructed. Leakage delay is used to
unstable the neuron states. It is a challenging task to develop delay dependent condition to estimate
the unstable neuron states through available sampled output measurements such that the error-state
system is globally asymptotically stable. By constructing Lyapunov–Krasovskii functional (LKF), a
sufficient condition depending on the sampling period is obtained in terms of linear matrix inequalities
(LMIs). Moreover, by using the free-weighting matrices method, simple and efficient criterion
is derived in terms of LMIs for estimation

Keywords

Journal of the Egyptian Mathematical Society
Volume 24, Issue 1
2016
Pages 143-150

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