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

P. Balasubramaniam¹  M. Kalpana² 

[1] Department of Mathematics, Gandhigram Rural Institute – Deemed University, Gandhigram 624 302, Tamil Nadu, India;Department of Mathematics, National Institute of Technology – Deemed University, Tiruchirappalli 620 015, Tamil Nadu, India;

关键词: Global asymptotical stability;    Fuzzy cellular neural networks;    Leakage delay;    Linear matrix inequalities;    Sample-data;    State estimation;   

DOI  :  10.1016/j.joems.2014.07.003

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown