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Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm

journal contribution
posted on 2018-02-01, 00:00 authored by S Hassan, M A Khanesar, J Jaafar, Abbas KhosraviAbbas Khosravi
An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets.

History

Journal

Neural computing and applications

Volume

29

Issue

4

Pagination

1001 - 1014

Publisher

Springer Verlag

Location

Berlin, Germany

ISSN

0941-0643

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2016, The Natural Computing Applications Forum