2-ℒ nonlinear system identification via recurrent neural networks

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9 Citations (Scopus)

Abstract

This paper proposes an ℒ2-ℒ identification scheme as a new robust identification method for nonlinear systems via recurrent neural networks. Based on linear matrix inequality (LMI) formulation, for the first time, the ℒ2-ℒ learning algorithm is presented to reduce the effect of disturbance to an ℒ2-ℒ induced norm constraint. New stability results, such as boundedness, input-to-state stability (ISS), and convergence, are established in some senses. It is shown that the design of the ℒ2-ℒ identification method can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed identification scheme.

Original languageEnglish
Pages (from-to)543-552
Number of pages10
JournalNonlinear Dynamics
Volume62
Issue number3
DOIs
Publication statusPublished - 2010 Nov

Keywords

  • Input-to-state stability (ISS)
  • Linear matrix inequality (LMI)
  • Recurrent neural networks
  • Weight learning law

ASJC Scopus subject areas

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

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