A new learning algorithm for a fully connected neuro-fuzzy inference system

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

Original languageEnglish
Article number6805169
Pages (from-to)1741-1757
Number of pages17
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number10
DOIs
StatePublished - 1 Oct 2014

Keywords

  • Fully connected neuro-fuzzy inference systems (F-CONFIS)
  • fuzzy logic
  • fuzzy neural networks
  • gradient descent
  • neural networks (NNs)
  • neuro-fuzzy system
  • optimal learning

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