Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach
Kalpana, M., Balasubramaniam, P. (2016). Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach. EKB Journal Management System, 24(1), 143-150. doi: 10.1016/j.joems.2014.07.003
M. Kalpana; P. Balasubramaniam. "Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach". EKB Journal Management System, 24, 1, 2016, 143-150. doi: 10.1016/j.joems.2014.07.003
Kalpana, M., Balasubramaniam, P. (2016). 'Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach', EKB Journal Management System, 24(1), pp. 143-150. doi: 10.1016/j.joems.2014.07.003
Kalpana, M., Balasubramaniam, P. Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach. EKB Journal Management System, 2016; 24(1): 143-150. doi: 10.1016/j.joems.2014.07.003

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, Page 143-150  XML PDF (593.59 K)
DOI: 10.1016/j.joems.2014.07.003
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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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