Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach | ||
Journal of the Egyptian Mathematical Society | ||
Volume 24, Issue 1, 2016, Pages 143-150 PDF (593.59 K) | ||
DOI: 10.1016/j.joems.2014.07.003 | ||
Authors | ||
M. Kalpana1; P. Balasubramaniam2 | ||
1Department of Mathematics, National Institute of Technology – Deemed University, Tiruchirappalli 620 015, Tamil Nadu, India | ||
2Department of Mathematics, Gandhigram Rural Institute – Deemed University, Gandhigram 624 302, Tamil Nadu, India | ||
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 | ||
Global asymptotical stability; Fuzzy cellular neural networks; Leakage delay; Linear matrix inequalities; Sample-data; State estimation | ||
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