Generating fuzzy rules by genetic algorithms

M. Mohammadian, R. J. Stonier

Research output: A Conference proceeding or a Chapter in BookConference contribution

20 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication
Place of PublicationPiscataway NJ, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages362-367
Number of pages6
ISBN (Electronic)0780320034
ISBN (Print)07780320026
DOIs
Publication statusPublished - 1994
Externally publishedYes
Event3rd IEEE International Workshop on Robot and Human Communication - Nagoya, Japan
Duration: 18 Jul 199420 Jul 1994

Conference

Conference3rd IEEE International Workshop on Robot and Human Communication
Abbreviated titleRo-Man
CountryJapan
CityNagoya
Period18/07/9420/07/94

Fingerprint

Fuzzy rules
Genetic algorithms
Controllers
Fuzzy logic
Trucks
Neural networks

Cite this

Mohammadian, M., & Stonier, R. J. (1994). Generating fuzzy rules by genetic algorithms. In Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication (pp. 362-367). Piscataway NJ, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ROMAN.1994.365902
Mohammadian, M. ; Stonier, R. J. / Generating fuzzy rules by genetic algorithms. Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication. Piscataway NJ, USA : IEEE, Institute of Electrical and Electronics Engineers, 1994. pp. 362-367
@inproceedings{af60c65237a741fe980fcd1e70d13906,
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",

}

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

Generating fuzzy rules by genetic algorithms. / Mohammadian, M.; Stonier, R. J.

Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication. Piscataway NJ, USA : IEEE, Institute of Electrical and Electronics Engineers, 1994. p. 362-367.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Generating fuzzy rules by genetic algorithms

AU - Mohammadian, M.

AU - Stonier, R. J.

PY - 1994

Y1 - 1994

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

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

ER -

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