On the approximation capability of neural networks-dynamic system modeling and control

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

1 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)122-130
Journal / PublicationAsian Journal of Control
Volume3
Issue number2
Publication statusPublished - Jun 2001

Abstract

This paper discusses issues related to the approximation capability of neural networks in modeling and control. We show that neural networks are universal models and universal controllers for a class of nonlinear dynamic systems. That is, for a given dynamic system, there exists a neural network which can model the system to any degree of accuracy over time. Moreover, if the system to be controlled is stabilized by a continuous controller, then there exists a neural network which can approximate the controller such that the system controlled by the neural network is also stabilized with a given bound of output error.

Research Area(s)

  • Neural networks, Systems modeling, Universal controllers