Lancaster EPrints

Automatic generation of fuzzy rule-based models from data by genetic algorithms.

Angelov, Plamen and Buswell, Richard (2003) Automatic generation of fuzzy rule-based models from data by genetic algorithms. Information Sciences—Informatics and Computer Science: An International Journal, 150 (1-2). pp. 17-31. ISSN 0020-0255

Full text not available from this repository.

Abstract

A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the paper. The encoding procedure is applied to the problem of automatically generating fuzzy rule-based models from data. Models generated by this approach have much of the flexibility of black-box methods, such as neural networks. In addition, they implicitly express information about the process being modelled through the linguistic terms associated with the rules. They can be applied to problems that are too complex to model in a first principles sense and can reduce the computational overhead when compared to established first principles based models. The encoding mechanism allows the rule base structure and parameters of the fuzzy model to be estimated simultaneously from data. The principle advantage is the preservation of the linguistic concept without the need to consider the entire rule base. The GA searches for the optimum solution given a comparatively small number of rules compared to all possible. This minimises the computational demand of the model generation and allows problems with realistic dimensions to be considered. A further feature is that the rules are extracted from the data without the need to establish any information about the model structure a priori. The implementation of the algorithm is described and the approach is applied to the modelling of components of heating ventilating and air-conditioning systems. (c) Information Sciences

Item Type: Article
Journal or Publication Title: Information Sciences—Informatics and Computer Science: An International Journal
Additional Information: The final, definitive version of this article has been published in the Journal, Information Sciences 150 (1-2), 2002, © ELSEVIER.
Uncontrolled Keywords: Fuzzy rule-based models ; Self learning ; Genetic algorithms ; Structure and parameter identification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 904
Deposited By: Dr. Plamen Angelov
Deposited On: 09 Jan 2008 08:58
Refereed?: Yes
Published?: Published
Last Modified: 21 Mar 2014 10:14
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/904

Actions (login required)

View Item