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http://dx.doi.org/10.5370/KIEE.2017.66.1.171

H∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control  

Liu, Yajuan (Dept. of Electrical Engineering, Yeungnam University)
Lee, Sangmoon (Dept. of Electronic Engineering, Kyungpook National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.66, no.1, 2017 , pp. 171-178 More about this Journal
Abstract
This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.
Keywords
State estimation; Neural networks; Time-varying delay; Non-fragile sampled-data control;
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