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

Can Isik, A. Mete Çakmakci

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 utilising 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 summarised. 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 publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages564-567
Number of pages4
Volume1993-January
ISBN (Print)0780309995
DOIs
StatePublished - 1993
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993

Other

OtherIEEE International Conference on Neural Networks, ICNN 1993
CountryUnited States
CitySan Francisco
Period3/28/934/1/93

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

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  • Cite this

    Isik, C., & Çakmakci, A. M. (1993). Identification of a nonlinear multivariable dynamic process using feed-forward networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1993-January, pp. 564-567). [298619] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNN.1993.298619