Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)

Chi-Hsu Wang*, Chun Sheng Cheng, Tsu Tian Lee

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process. It can also be shown both learning rates can not be both negative. Excellent results are obtained for the truck backing-up control, which yield more improved performance than those using type-1 FNN.

Original languageEnglish
Pages (from-to)3663-3668
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
StatePublished - 24 Nov 2003
EventSystem Security and Assurance - Washington, DC, United States
Duration: 5 Oct 20038 Oct 2003


  • Back propagation
  • Dynamic optimal learning rate
  • Interval type-2 FNN

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