On the BP training algorithm of fuzzy neural networks (FNNs) via its equivalent fully connected neural networks (FFNNs)

Jing Wang*, Chi-Hsu Wang, C. L.Philip Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, Fuzzy Neural Network (FNN) is first transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, BP training algorithm is derived to tune both the premise and consequent part of FNN. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation achieves satisfactory results. Developing BP training algorithm for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing..., etc.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages1376-1381
Number of pages6
DOIs
StatePublished - 23 Dec 2011
Event2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States
Duration: 9 Oct 201112 Oct 2011

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
CountryUnited States
CityAnchorage, AK
Period9/10/1112/10/11

Keywords

  • Back Propagations
  • Fuzzy Logic
  • Fuzzy Neural Networks
  • Gradient Descent
  • Neural Networks

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