A new robust training law for dynamic neural networks with external disturbance: An LMI approach

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Abstract

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

Original languageEnglish
Article number415895
JournalDiscrete Dynamics in Nature and Society
Volume2010
DOIs
Publication statusPublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Modelling and Simulation

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