Neuro-fuzzy control using self-organizing neural nets

Farrukh Zia, Can Isik

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

3 Scopus citations

Abstract

This paper discusses a new approach to design a fuzzy logic control system, based on the self-organizing map (SOM) neural network. SOM is used to generate multivariate fuzzy state space from system's input-output data through unsupervised training. The trained SOM is then used as a part of an inference mechanism for a fuzzy logic controller. The proposed method is compared with other fuzzy/NN approaches. Sample data from a chemical plant is used to demonstrate the technique.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE Computer Society
Pages70-75
Number of pages6
Volume1
StatePublished - 1994
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA
Duration: Jun 26 1994Jun 29 1994

Other

OtherProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
CityOrlando, FL, USA
Period6/26/946/29/94

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Zia, F., & Isik, C. (1994). Neuro-fuzzy control using self-organizing neural nets. In IEEE International Conference on Fuzzy Systems (Vol. 1, pp. 70-75). IEEE Computer Society.