An input-to-state stability approach to filter design for neural networks with noise disturbance

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, an input-to-state stability (ISS) approach is introduced to derive a new state estimation filter for Hopfield neural networks with noise disturbance. A new ISS filtering method is developed such that the filtering error system is exponentially stable and input-to-state stable for the noise disturbance. The proposed filter can be obtained by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.

Original languageEnglish
Pages (from-to)275-278
Number of pages4
JournalAdvanced Science Letters
Volume5
Issue number1
DOIs
Publication statusPublished - 2012 Jan 1
Externally publishedYes

Keywords

  • Hopfield neural networks
  • Input-to-state stability (ISS)
  • Linear matrix inequality (LMI)
  • Noise disturbance
  • State estimation filter

ASJC Scopus subject areas

  • Education
  • Health(social science)
  • Mathematics(all)
  • Energy(all)
  • Computer Science(all)
  • Environmental Science(all)
  • Engineering(all)

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