A study on H state estimation of static neural networks with time-varying delays

Yajuan Liu, S. M. Lee, O. M. Kwon, Ju H. Park

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

Abstract

This paper studies the problem of H state estimation for static neural networks with time-varying delay. By construction of a suitable Lyapunov-Krasovskii functional, some improved delay-dependent conditions are established such that the error system is globally exponentially stable with a decay rate and a prescribed H performance is guaranteed. In order to get less conservative results of the state estimation condition, zero equalities and reciprocally convex approach are employed. The estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities. Numerical examples are provided to illustrate the effectiveness and performance of the developed method.

Original languageEnglish
Pages (from-to)589-597
Number of pages9
JournalApplied Mathematics and Computation
Volume226
DOIs
StatePublished - 2014

Keywords

  • H Performance
  • State estimation
  • Static neural network

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