2 learning of dynamic neural networks

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2 Citations (Scopus)


This paper proposes an ℒ2 learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the ℒ2 learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an ℒ2 induced norm constraint. It is shown that the design of the ℒ2 learning law for such neural networks 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 learning law.

Original languageEnglish
Article number100201
JournalChinese Physics B
Issue number10
Publication statusPublished - 2010 Oct
Externally publishedYes


  • Dynamic neural networks
  • Linear matrix inequality
  • Lyapunov stability theory
  • ℒ ℒ learning law

ASJC Scopus subject areas

  • General Physics and Astronomy


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