Fuzzy gaussian potential neural networks using a functional reasoning

Mohammad Teshnehlab, Keigo Watanabe

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

1 Citation (Scopus)

Abstract

This paper presents the principal design of a fuzzy gaussian potential neural network (FGPNN) to achieve high capability to learn expert control rules of the fuzzy controller. In this construction, each membership function consists of a gaussian potential function (GPF) which causes the utilization of a reduced number of labels, and eventually the complexity of structural design becomes simple, specially for large scale inputs, This in turn reduces the learning trials, to improve the learning speed. Thus, the time of the training process, which is based on the ba~k-propagation method, is shortened. The construction of an FGPNN is carried out with the minimum number of GPF, based on the number of input patterns, to learn the mean vectors and shapes of the individual GPFs that basically depend on the desired trajectory. Finally, we provide a simulation to evaluate the proposed method for a multi input-output, twolink manipulator.

Original languageEnglish
Title of host publicationAdvances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers
EditorsTakeshi Furuhashi
PublisherSpringer Verlag
Pages34-47
Number of pages14
ISBN (Print)9783540606079
DOIs
Publication statusPublished - Jan 1 1995
Externally publishedYes
Event3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994 - Nagoya, Japan
Duration: Aug 9 1994Aug 10 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1011
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994
CountryJapan
CityNagoya
Period8/9/948/10/94

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Teshnehlab, M., & Watanabe, K. (1995). Fuzzy gaussian potential neural networks using a functional reasoning. In T. Furuhashi (Ed.), Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers (pp. 34-47). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1011). Springer Verlag. https://doi.org/10.1007/3-540-60607-6_3