Evolutionary computing for fuzzy system optimization in intelligent control

被引：0
|
作者：
Castillo, O [1 ]
Huesca, G [1 ]
Valdez, F [1 ]
机构：
[1] Tijuana Inst Technol, Dept Comp Sci, Tijuana, Mexico
来源：
IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS | 2004年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We describe in this paper the use of hierarchical genetic algorithms for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The hierarchical genetic algorithm enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with the case of intelligent control in a medical application. Simulation results for this application show that we are able to find an optimal set of rules and membership functions for the fuzzy system.
引用
收藏
页码：98 / 104
页数：7
相关论文
共 50 条
  • [31] An intelligent cooperative control system based on predictive fuzzy control
    Yasunobu, S
    Okamoto, Y
    [J]. SICE 2004 ANNUAL CONFERENCE, VOLS 1-3, 2004, : 1896 - 1900
  • [32] Study of Intelligent Greenhouse Control System Based on Fuzzy Control
    Liu Jinhua
    Yu Guibo
    Tian Guang
    Ma Qiao
    [J]. ISTM/2009: 8TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, 2009, : 2406 - 2408
  • [33] An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control
    Song, Lepeng
    Huang, Jinpen
    Liang, Xianwen
    Yang, Simon X.
    Hu, Wenjin
    Tang, Dedong
    [J]. SENSORS, 2020, 20 (10)
  • [34] Synthesis of Train Traffic Control System with Evolutionary Computing
    Kilyen, Attila Ors
    Hulea, Mihai
    Letia, Tiberiu S.
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS, 2014,
  • [35] Evolutionary fuzzy logic system for intelligent fibre optic components assembly
    Pham, DT
    Castellani, M
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2002, 216 (05) : 571 - 581
  • [36] Neuro-fuzzy hybrid intelligent system using grid computing
    Ahmed, Laeeq
    Shah, Syed Adeel Ali
    [J]. THIRD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES 2007, PROCEEDINGS, 2007, : 145 - 147
  • [37] Economic Load Dispatch using Intelligent Optimization with Fuzzy Control
    Lai, Johnny C. Y.
    Leung, Frank H. F.
    Ling, Sai-Ho
    Shi, Edwin C.
    [J]. IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011), 2011, : 2219 - 2224
  • [38] Evolutionary computation based optimization in fuzzy automatic generation control
    Roy, Ranjit
    Ghoshal, S. P.
    [J]. 2006 IEEE POWER INDIA CONFERENCE, VOLS 1 AND 2, 2006, : 249 - +
  • [39] Evolutionary Fuzzy Scheduler for Grid Computing
    Prado, R. P.
    Garcia Galan, S.
    Yuste, A. J.
    Munoz Exposito, J. E.
    Sanchez Santiago, A. J.
    Bruque, S.
    [J]. BIO-INSPIRED SYSTEMS: COMPUTATIONAL AND AMBIENT INTELLIGENCE, PT 1, 2009, 5517 : 286 - 293
  • [40] Multiphase adaptive fuzzy control for intelligent transportation system
    Ma Wen-ge
    [J]. ICICIC 2006: FIRST INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING, INFORMATION AND CONTROL, VOL 2, PROCEEDINGS, 2006, : 565 - 569
12345