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 journalpeer-review

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


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