The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process

Thi Nguyen; Lee Gordon-Brown; Jim Peterson

doi:10.1109/CIMCA.2008.40

The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process

Thi Nguyen, Lee Gordon-Brown, Jim Peterson

Econometrics & Business Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

A hybrid approach of learning process is investigated to optimize the fuzzy rule structure of the fuzzy system for function approximation. First, if-then rules are initialized more much than usual and then are optimized via deployment of a genetic algorithm. Subsequently, the supervised gradient descent algorithm (incorporated momentum technique) is utilized in order to tune the fuzzy rule parameters. Experimental results are presented that indicate significant improvement in term of accuracy in function approximation can be achieved during deployment of the Standard Additive Model (SAM) by adopting the hybrid approach

Original language	English
Title of host publication	2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1211-1216
Number of pages	6
ISBN (Print)	9780769535142
DOIs	https://doi.org/10.1109/CIMCA.2008.40
Publication status	Published - 2008
Event	International Conference on Computational Intelligence for Modelling, Control and Automation 2008 - Vienna, Austria Duration: 10 Dec 2008 → 12 Dec 2008

Conference

Conference	International Conference on Computational Intelligence for Modelling, Control and Automation 2008
Abbreviated title	CIMCA 2008
Country/Territory	Austria
City	Vienna
Period	10/12/08 → 12/12/08

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10.1109/CIMCA.2008.40

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Nguyen, T., Gordon-Brown, L., & Peterson, J. (2008). The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process. In 2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008 (pp. 1211-1216). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CIMCA.2008.40

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title = "The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process",

abstract = "A hybrid approach of learning process is investigated to optimize the fuzzy rule structure of the fuzzy system for function approximation. First, if-then rules are initialized more much than usual and then are optimized via deployment of a genetic algorithm. Subsequently, the supervised gradient descent algorithm (incorporated momentum technique) is utilized in order to tune the fuzzy rule parameters. Experimental results are presented that indicate significant improvement in term of accuracy in function approximation can be achieved during deployment of the Standard Additive Model (SAM) by adopting the hybrid approach",

author = "Thi Nguyen and Lee Gordon-Brown and Jim Peterson",

year = "2008",

doi = "10.1109/CIMCA.2008.40",

language = "English",

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pages = "1211--1216",

booktitle = "2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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note = "International Conference on Computational Intelligence for Modelling, Control and Automation 2008, CIMCA 2008 ; Conference date: 10-12-2008 Through 12-12-2008",

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Nguyen, T, Gordon-Brown, L & Peterson, J 2008, The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process. in 2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008. IEEE, Institute of Electrical and Electronics Engineers, pp. 1211-1216, International Conference on Computational Intelligence for Modelling, Control and Automation 2008, Vienna, Austria, 10/12/08. https://doi.org/10.1109/CIMCA.2008.40

The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process. / Nguyen, Thi; Gordon-Brown, Lee; Peterson, Jim.
2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008. IEEE, Institute of Electrical and Electronics Engineers, 2008. p. 1211-1216.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Gordon-Brown, Lee

AU - Peterson, Jim

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AB - A hybrid approach of learning process is investigated to optimize the fuzzy rule structure of the fuzzy system for function approximation. First, if-then rules are initialized more much than usual and then are optimized via deployment of a genetic algorithm. Subsequently, the supervised gradient descent algorithm (incorporated momentum technique) is utilized in order to tune the fuzzy rule parameters. Experimental results are presented that indicate significant improvement in term of accuracy in function approximation can be achieved during deployment of the Standard Additive Model (SAM) by adopting the hybrid approach

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ER -

The optimal rule structure for Fuzzy systems in function approximation by hybrid approach in learning process

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