The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control system
β¦ LIBER β¦
Tuning of a neuro-fuzzy controller by genetic algorithm
β Scribed by Teo Lian Seng; Bin Khalid, M.; Yusof, R.
- Book ID
- 117874484
- Publisher
- IEEE
- Year
- 1999
- Tongue
- English
- Weight
- 285 KB
- Volume
- 29
- Category
- Article
- ISSN
- 1083-4419
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Tuning fuzzy logic controllers by geneti
β
F. Herrera; M. Lozano; J.L. Verdegay
π
Article
π
1995
π
Elsevier Science
π
English
β 692 KB
A new approach of neuro-fuzzy learning a
β
Yan Shi; Masaharu Mizumoto
π
Article
π
2000
π
Elsevier Science
π
English
β 609 KB
A genetic-algorithm-based method for tun
β
H.B. GΓΌrocak
π
Article
π
1999
π
Elsevier Science
π
English
β 234 KB
It has been demonstrated many times in practice that fuzzy logic controllers have an important role in rule-based expert systems. However, it is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic con
An improvement of neuro-fuzzy learning a
β
Yan Shi; Masaharu Mizumoto
π
Article
π
2001
π
Elsevier Science
π
English
β 125 KB
Emergent behaviors of a fuzzy sensory-mo
β
Seung-Ik Lee; Sung-Bae Cho
π
Article
π
2001
π
IEEE
π
English
β 410 KB
A fuzzy self-tuning parallel genetic alg
β
Chin-Chih Hsu; Shin-Ichi Yamada; Hideji Fujikawa; Koichiro Shida
π
Article
π
1996
π
Elsevier Science
π
English
β 551 KB