Generating fuzzy rules by genetic algorithms

M. Mohammadian; R. J. Stonier

doi:10.1109/ROMAN.1994.365902

Generating fuzzy rules by genetic algorithms

M. Mohammadian, R. J. Stonier

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

22 Citations (Scopus)

823 Downloads (Pure)

Abstract

A general method is developed to generate fuzzy rules by using Genetic Algorithms (GAs) and a Fuzzy Logic controller (FLC). By using GAs as a learning procedure and a FLC as the system's performance evaluator, the proposed architecture can construct an input-output mapping in the form of fuzzy If-Then rules. The performance of the new architecture is compared with an artificial neural networks controller and pure limited-rule fuzzy rule controller for the truck back-upper problem.

Original language	English
Title of host publication	Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication
Place of Publication	Piscataway NJ, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	362-367
Number of pages	6
ISBN (Electronic)	0780320034
ISBN (Print)	07780320026
DOIs	https://doi.org/10.1109/ROMAN.1994.365902
Publication status	Published - 1994
Externally published	Yes
Event	3rd IEEE International Workshop on Robot and Human Communication - Nagoya, Japan Duration: 18 Jul 1994 → 20 Jul 1994

Conference

Conference	3rd IEEE International Workshop on Robot and Human Communication
Abbreviated title	Ro-Man
Country/Territory	Japan
City	Nagoya
Period	18/07/94 → 20/07/94

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10.1109/ROMAN.1994.365902

PaperFinal published version, 6.38 MB

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title = "Generating fuzzy rules by genetic algorithms",

abstract = "A general method is developed to generate fuzzy rules by using Genetic Algorithms (GAs) and a Fuzzy Logic controller (FLC). By using GAs as a learning procedure and a FLC as the system's performance evaluator, the proposed architecture can construct an input-output mapping in the form of fuzzy If-Then rules. The performance of the new architecture is compared with an artificial neural networks controller and pure limited-rule fuzzy rule controller for the truck back-upper problem.",

keywords = "Genetic algorithms, Fuzzy control, Fuzzy logic, Aritificial neural networks, Humans, Fuzzy systems, Fuzzy neural networks, Data mining, Fuzzy sets, Genetic mutations",

author = "M. Mohammadian and Stonier, {R. J.}",

year = "1994",

doi = "10.1109/ROMAN.1994.365902",

language = "English",

isbn = "07780320026",

pages = "362--367",

booktitle = "Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication",

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

address = "United States",

note = "3rd IEEE International Workshop on Robot and Human Communication, Ro-Man ; Conference date: 18-07-1994 Through 20-07-1994",

}

Mohammadian, M & Stonier, RJ 1994, Generating fuzzy rules by genetic algorithms. in Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ, USA, pp. 362-367, 3rd IEEE International Workshop on Robot and Human Communication, Nagoya, Japan, 18/07/94. https://doi.org/10.1109/ROMAN.1994.365902

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AU - Mohammadian, M.

AU - Stonier, R. J.

PY - 1994

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N2 - A general method is developed to generate fuzzy rules by using Genetic Algorithms (GAs) and a Fuzzy Logic controller (FLC). By using GAs as a learning procedure and a FLC as the system's performance evaluator, the proposed architecture can construct an input-output mapping in the form of fuzzy If-Then rules. The performance of the new architecture is compared with an artificial neural networks controller and pure limited-rule fuzzy rule controller for the truck back-upper problem.

AB - A general method is developed to generate fuzzy rules by using Genetic Algorithms (GAs) and a Fuzzy Logic controller (FLC). By using GAs as a learning procedure and a FLC as the system's performance evaluator, the proposed architecture can construct an input-output mapping in the form of fuzzy If-Then rules. The performance of the new architecture is compared with an artificial neural networks controller and pure limited-rule fuzzy rule controller for the truck back-upper problem.

KW - Genetic algorithms

KW - Fuzzy control

KW - Fuzzy logic

KW - Aritificial neural networks

KW - Humans

KW - Fuzzy systems

KW - Fuzzy neural networks

KW - Data mining

KW - Fuzzy sets

KW - Genetic mutations

UR - http://www.scopus.com/inward/record.url?scp=0028570966&partnerID=8YFLogxK

U2 - 10.1109/ROMAN.1994.365902

DO - 10.1109/ROMAN.1994.365902

M3 - Conference contribution

AN - SCOPUS:0028570966

SN - 07780320026

SP - 362

EP - 367

BT - Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ, USA

T2 - 3rd IEEE International Workshop on Robot and Human Communication

Y2 - 18 July 1994 through 20 July 1994

ER -

Generating fuzzy rules by genetic algorithms

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