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Evolving Fuzzy Rule-based Models

Angelov, Plamen (2000) Evolving Fuzzy Rule-based Models. Journal of the Chinese Institute of Industrial Engineers, 17 (5). pp. 459-468. ISSN 1017-0669

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Abstract

An approach of evolving fuzzy rule-based models by genetic algorithms (GA) is proposed in the paper. Both structure of the model (fuzzy rules) and parameters of the fuzzy membership functions of the linguistic variables are generated automatically. The main feature of the proposed approach is the new encoding mechanism of the chromosome that is more efficient than encoding used in previous evolutionary learning methods. The representation does not need all the rules to be present because the GA selects a small subset of the used rules only. This fact leads to minimizing the computational load using significantly smaller chromosome and real-coded GA, making possible simultaneous parameter and structural identification. Evolving fuzzy rule-based models need only inputs and outputs to be known, but unlike the other typical black-box models (neural networks, polynomial models etc.) their transparency is very high due to the design of linguistic rules during the process of knowledge extraction and aggregation. Two practical building services engineering problems are considered in order to illustrate the applicability of the approach.

Item Type: Article
Journal or Publication Title: Journal of the Chinese Institute of Industrial Engineers
Uncontrolled Keywords: evolving fuzzy systems ; Fuzzy rule-based models ; self-learning ; genetic algorithms ; structure ; parameter identification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 56250
Deposited By: ep_importer_pure
Deposited On: 27 Jul 2012 09:52
Refereed?: Yes
Published?: Published
Last Modified: 18 Dec 2014 12:22
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/56250

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