Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach

Adrian Rubio-Solis, Uriel Martinez Hernandez, George Panoutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

It has been demonstrated that 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 tradeoff 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 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 provide 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.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Number of pages8
ISBN (Print)978-1-5090-6020-7
DOIs
Publication statusPublished - 15 Oct 2018

Cite this

Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach. / Rubio-Solis, Adrian; Martinez Hernandez, Uriel; Panoutsos, George.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. 8491583.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rubio-Solis, Adrian ; Martinez Hernandez, Uriel ; Panoutsos, George. / Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018.
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abstract = "It has been demonstrated that Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for theparameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher tradeoff 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 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 provide a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to purefuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency.",
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AB - It has been demonstrated that Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for theparameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher tradeoff 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 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 provide a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to purefuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency.

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