Hybrid model for the training of interval type-2 fuzzy logic system

Hassan, S. and Khosravi, A. and Jaafar, J. and Khanesar, M.A. (2015) Hybrid model for the training of interval type-2 fuzzy logic system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9489. pp. 644-653. ISSN 03029743

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Abstract

In this paper, a hybrid training model for interval type-2 fuzzy logic system is proposed. The hybrid training model uses extreme learning machine to tune the consequent part parameters and genetic algorithm to optimize the antecedent part parameters. The proposed hybrid learning model of interval type-2 fuzzy logic system is tested on the prediction of Mackey-Glass time series data sets with different levels of noise. The results are compared with the existing models in literature; extreme learning machine and Kalman filter based learning of consequent part parameters with randomly generated antecedent part parameters. It is observed that the interval type-2 fuzzy logic system provides improved performance with the proposed hybrid learning model. © Springer International Publishing Switzerland 2015.

Item Type: Article
Additional Information: cited By 3; Conference of 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference Date: 9 November 2015 Through 12 November 2015; Conference Code:157859
Uncontrolled Keywords: Computer circuits; Forecasting; Genetic algorithms; Information science; Knowledge acquisition; Learning algorithms; Learning systems; Reconfigurable hardware, Extreme learning machine; Hybrid learning; Hybrid model; Hybrid training; Interval type-2 fuzzy logic systems; Mackey-glass time-series datum, Fuzzy logic
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:17
Last Modified: 09 Nov 2023 16:17
URI: https://khub.utp.edu.my/scholars/id/eprint/6171

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