A Genetic-Based Method for Learning the Parameters of a Fuzzy Inference System

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	Similar Articles Articles by Florin Fagarasan Articles by Mircea G. Negoita

2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95)

Dunedin, New Zealand

November 20-November 23

ISBN: 0-8186-7174-2

	ASCII Text	x
	Florin Fagarasan, Mircea G. Negoita, "A Genetic-Based Method for Learning the Parameters of a Fuzzy Inference System," Artificial Neural Networks and Expert Systems, New Zealand Conference, pp. 223, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995.

	BibTex	x
	@article{ 10.1109/ANNES.1995.499476, author = {Florin Fagarasan and Mircea G. Negoita}, title = {A Genetic-Based Method for Learning the Parameters of a Fuzzy Inference System}, journal ={Artificial Neural Networks and Expert Systems, New Zealand Conference}, volume = {0}, year = {1995}, isbn = {0-8186-7174-2}, pages = {223}, doi = {http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499476}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Artificial Neural Networks and Expert Systems, New Zealand Conference TI - A Genetic-Based Method for Learning the Parameters of a Fuzzy Inference System SN - 0-8186-7174-2 SP EP A1 - Florin Fagarasan, A1 - Mircea G. Negoita, PY - 1995 KW - fuzzy inference system KW - inductive learning KW - genetic algorithms KW - variable length genotype VL - 0 JA - Artificial Neural Networks and Expert Systems, New Zealand Conference ER -

Florin Fagarasan, Institute of Microtechnology

Mircea G. Negoita, Institute of Microtechnology

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499476

Fuzzy inference systems (FIS) provide models for approximating continuous, real valued functions. The successful application of fuzzy reasoning models depends on a number of parameters, such as the fuzzy partition of the input/output universes of discourse, that are usually decided in a subjective manner (traditionally, fuzzy rule bases was constructed by knowledge acquisition from human experts). The aim of this paper is to present a flexible genetic based method for learning the parameters of a FIS from examples such as the subjectivity not to be involved at all. We will show that applying this method it is possible to obtain better performances for the FIS or, at the same performances, a less complex structure for the system.

Index Terms:

fuzzy inference system, inductive learning, genetic algorithms, variable length genotype

Citation:

Florin Fagarasan, Mircea G. Negoita, "A Genetic-Based Method for Learning the Parameters of a Fuzzy Inference System," annes, pp.223, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995

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