Takagi-Sugeno fuzzy hopfield neural networks for H nonlinear system identification

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

In this paper, we propose a new H weight learning algorithm (HWLA) for nonlinear system identification via Takagi.Sugeno (T.S) fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, for the first time, the HWLA fornonlinear system identification is presented to reduce the effect of disturbance to an H norm constraint. The HWLA can be obtained by solving a convex optimization problem which is represented in terms of linear matrix inequality (LMI). An illustrative example is given to demonstrate the effectiveness of the proposed identification scheme.

Original languageEnglish
Pages (from-to)59-70
Number of pages12
JournalNeural Processing Letters
Volume34
Issue number1
DOIs
Publication statusPublished - 2011 Aug 1
Externally publishedYes

Fingerprint

Hopfield neural networks
Fuzzy neural networks
Learning algorithms
Nonlinear systems
Identification (control systems)
Learning
Weights and Measures
Convex optimization
Linear matrix inequalities
Time delay

Keywords

  • H nonlinear system identification
  • Linear matrix inequality (LMI)
  • Lyapunov stabilitytheory
  • Takagi-Sugeno fuzzy Hopfield neural networks
  • Weight learning algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications
  • Neuroscience(all)

Cite this

Takagi-Sugeno fuzzy hopfield neural networks for H nonlinear system identification. / Ahn, Choon Ki.

In: Neural Processing Letters, Vol. 34, No. 1, 01.08.2011, p. 59-70.

Research output: Contribution to journalArticle

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