The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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H∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control

비결함 샘플 데이타 제어를 가지는 정적 지연 뉴럴 네트웍의 강인 상태추정

  • Liu, Yajuan (Dept. of Electrical Engineering, Yeungnam University) ;
  • Lee, Sangmoon (Dept. of Electronic Engineering, Kyungpook National University)
  • Received : 2016.11.25
  • Accepted : 2016.12.15
  • Published : 2017.01.01
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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.

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References

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