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

• Main Authors: M. Kalpana, P. Balasubramaniam
• Format: Article
• Language: English
• Published: SpringerOpen 2016-01-01
• Series: Journal of the Egyptian Mathematical Society
• Subjects: Global asymptotical stability; Fuzzy cellular neural networks; Leakage delay; Linear matrix inequalities; Sample-data; State estimation
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110256X1400100X

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.