General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model

Adrian Rubio-Solis, Patricia Melin, Uriel Martinez-Hernandez, George Panoutsos

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33 Citations (SciVal)
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This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the α-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are used. Hence, this paper provides four different architectures of the GT2-RBFNN and their parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation (IIG) approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is optimised using an Adaptive Gradient Descent method (AGD) respectively. A number of popular benchmark data sets, the identification of a nonlinear system and the prediction of chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies.

Original languageEnglish
Article number8417444
Pages (from-to)333-347
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Issue number2
Early online date23 Jul 2018
Publication statusPublished - 1 Feb 2019


  • α-plane representation
  • Frequency selective surfaces
  • Fuzzy logic
  • fuzzy modelling
  • Fuzzy sets
  • General Type-2 FLSs
  • Iterative algorithms
  • Radial basis function networks
  • Radial Basis Function Neural Networks
  • Uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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