Application of dynamic neural networks to approximation and control of nonlinear systems

S. Massoud Amin, Ervin Y. Rodin, Alan Y. Wu

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Based on a paradigm of neurons with local memory (NLM), we discuss the representation of control systems by neural networks. Using this formulation, the basic issues of controllability and observability for the dynamic system are addressed. A separation principle of learning and control is presented for NLM, showing that the weights of the network do not affect its dynamics. Theoretical issues concerning local linearization via a coordinate transformation and nonlinear feedback are discussed.

Original languageEnglish (US)
Pages (from-to)222-226
Number of pages5
JournalProceedings of the American Control Conference
Volume1
StatePublished - Jan 1 1997
Externally publishedYes
EventProceedings of the 1997 American Control Conference. Part 3 (of 6) - Albuquerque, NM, USA
Duration: Jun 4 1997Jun 6 1997

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