Maximum entropy radial basis function network based neuro-fuzzy controller

Jiann Horng Lin, Can Isik

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

11 Scopus citations

Abstract

This paper presents a systematic approach to constructing a self-organizing fuzzy controller. The proposed controller is built on a neuro-fuzzy system consisting of a maximum entropy self-organizing net (MESON) and a radial basis function network (RBFN). We develop the corresponding self-organizing algorithms. MESON, a new fuzzy clustering neural network model, combines the ideas of fuzzy membership values for learning rates based on the maximum entropy principle, and the structure and update rules of Kohonen clustering network (KCN). The strategy proposed in our approach for the update rules of KCN is derived from the fixed-point iteration for the solution of nonlinear equations. This model eliminates the sensitivity to the choice of the initial configuration and yields a dynamic fuzzy clustering solution. MESON is used for the generation of fuzzy rules as well as the construction of RBFN for fuzzy inference.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE Computer Society
Pages156-161
Number of pages6
Volume1
StatePublished - 1996
EventProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3) - New Orleans, LA, USA
Duration: Sep 8 1996Sep 11 1996

Other

OtherProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3)
CityNew Orleans, LA, USA
Period9/8/969/11/96

ASJC Scopus subject areas

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

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  • Cite this

    Lin, J. H., & Isik, C. (1996). Maximum entropy radial basis function network based neuro-fuzzy controller. In IEEE International Conference on Fuzzy Systems (Vol. 1, pp. 156-161). IEEE Computer Society.