Switched exponential state estimation of neural networks based on passivity theory

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69 Citations (Scopus)

Abstract

In this paper, a new exponential state estimation method is proposed for switched Hopfield neural networks based on passivity theory. Through available output measurements, the main purpose is to estimate the neuron states such that the estimation error system is exponentially stable and passive from the control input to the output error. Based on augmented Lyapunov-Krasovskii functional, Jensen's inequality, and linear matrix inequality (LMI), a new delay-dependent state estimator for switched Hopfield neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving delay-dependent LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)573-586
Number of pages14
JournalNonlinear Dynamics
Volume67
Issue number1
DOIs
Publication statusPublished - 2012 Jan 1
Externally publishedYes

Keywords

  • Augmented Lyapunov-Krasovskii functional
  • Linear matrix inequality (LMI)
  • Passivity theory
  • State estimation
  • Switched Hopfield neural networks

ASJC Scopus subject areas

  • Applied Mathematics
  • Mechanical Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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