Identification of a nonlinear multivariable dynamic process using feed-forward networks

Can Isik, Mete A. Cakmakci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The practical aspects of identifying a nonlinear multi-input multi-output dynamic system using feed-forward neural networks (NN) is the subject of this paper. By utilizing the measurements of 25 input and internal variables of the process, the primary process output is estimated with a network that has one hidden layer and partial connectivity. Two different connectivity patterns are compared, and problems encountered during the development are summarized. The accuracy of the estimate is demonstrated by comparing the NN output with the process output in time domain and frequency domain.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
Editors Anon
PublisherIEEE Computer Society
Pages564-567
Number of pages4
ISBN (Print)0780312007
StatePublished - 1993
Event1993 IEEE International Conference on Neural Networks - San Francisco, CA, USA
Duration: Mar 28 1993Apr 1 1993

Other

Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, CA, USA
Period3/28/934/1/93

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Isik, C., & Cakmakci, M. A. (1993). Identification of a nonlinear multivariable dynamic process using feed-forward networks. In Anon (Ed.), 1993 IEEE International Conference on Neural Networks (pp. 564-567). IEEE Computer Society.