Delay-dependent H∞ state estimation of neural networks with mixed time-varying delays

S. Lakshmanan, K. Mathiyalagan, Ju H. Park, R. Sakthivel, Fathalla A. Rihan

Research output: Contribution to journalArticlepeer-review

66 Citations (Scopus)

Abstract

In this paper, the delay-dependent H∞ state estimation of neural networks with a mixed time-varying delay is considered. By constructing a suitable Lyapunov-Krasovskii functional with triple integral terms and using Jensen inequality and linear matrix inequality (LMI) framework, the delay-dependent criteria are presented so that the error system is globally asymptotically stable with H∞ performance. The activation functions are assumed to satisfy sector-like nonlinearities. The estimator gain matrix for delayed neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. Finally a numerical example with simulation is presented to demonstrate the usefulness and effectiveness of the obtained results.

Original languageEnglish
Pages (from-to)392-400
Number of pages9
JournalNeurocomputing
Volume129
DOIs
Publication statusPublished - Apr 10 2014

Keywords

  • Guaranteed performance
  • H∞ estimation
  • Mixed time-varying delays
  • Neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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