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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2018

01.08.2016 | Original Article

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

verfasst von: Saima Hassan, Mojtaba Ahmadieh Khanesar, Jafreezal Jaafar, Abbas Khosravi

Erschienen in: Neural Computing and Applications | Ausgabe 4/2018

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Abstract

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.
Vorheriger Artikel A novel construction of substitution box for image encryption applications with Gingerbreadman chaotic map and S8 permutation
Nächster Artikel Evolutionary framework for coding area selection from cancer data

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Metadaten
Titel
Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm
verfasst von
Saima Hassan
Mojtaba Ahmadieh Khanesar
Jafreezal Jaafar
Abbas Khosravi
Publikationsdatum
01.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2503-5

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