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Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model.
In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model.
These parameters are learned simultaneously.
The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods.
The proposed CS method gives an optimal T–S fuzzy model with fewer numbers of rules.
American Psychological Association (APA)
Turki, Mourad& Sakly, Anis. 2017. Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141198
Modern Language Association (MLA)
Turki, Mourad& Sakly, Anis. Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141198
American Medical Association (AMA)
Turki, Mourad& Sakly, Anis. Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141198
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141198