Rubio-Solis, A., Martinez-Hernandez, U. and Panoutsos, G. (2018) Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 08-13 Jul 2018, Rio de Janeiro, Brazil. IEEE ISBN 978-1-5090-6020-7
Abstract
Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for the parameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher trade-off between accuracy and model simplicity. For that reason, this paper shows that an E-ELM that is based on Particle Swarm Optimisation (PSO) and Extreme Learning machine (ELM) can be extended to the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) with a Karnik-Mendel type-reduction layer. To evaluate the efficiency of E-ELM, the IT2-RBFNN is used as an Interval Type-2 Fuzzy Logic System (IT2 FLS) for the modelling of two popular benchmark data sets and for the prediction of chaotic time series. According to our results, E-ELM applied to the IT2-RBFNN not only outperforms adaptive-gradient-based algorithms and provides a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to pure fuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency.
Metadata
Item Type: | Proceedings Paper |
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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: | Interval type-2 fuzzy logic systems; RBF neural networks; extreme learning machine; Particle Swarm Optimisation (PSO); 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: | 12 Apr 2018 10:11 |
Last Modified: | 03 Jan 2019 12:11 |
Published Version: | https://doi.org/10.1109/FUZZ-IEEE.2018.8491583 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/FUZZ-IEEE.2018.8491583 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129567 |