Rubio-Solis, A., Melin, P., Martinez-Hernandez, U. et al. (1 more author) (2018) General type-2 radial basis function neural network: a data-driven fuzzy model. IEEE Transactions on Fuzzy Systems. ISSN 1063-6706
Abstract
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 alpha-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 employed. For that reason, this paper provides four different neural structures of the GT2-RBFNN and their structural and parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is trained and optimised using an Adaptive Gradient Descent method (AGD) respectively. Several benchmark data sets, including a problem of identification of a nonlinear system and a 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | General Type-2 FLSs; Radial Basis Function Neural Networks; α-plane representation; fuzzy modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Jul 2018 09:46 |
Last Modified: | 04 Sep 2018 15:19 |
Published Version: | https://doi.org/10.1109/TFUZZ.2018.2858740 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TFUZZ.2018.2858740 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133862 |