Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms

Chuen Yau Chen, Bin Da Liu, Ju Ying Tsao

研究成果: Paper

3 引文 (Scopus)

摘要

This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goal. The genetic algorithms search the optimal linguistic hedge combination in the linguistic hedge module. Accordingly, the linguistic hedge fuzzy logic controller has advantages: 1) it needs only the simple-shape membership functions for characterizing the related variables; 2) it is sufficient to adopt less number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design two well-known nonlinear systems. The simulation results demonstrate the effectiveness of this design.

原文English
頁面III-1299 - III-1304
出版狀態Published - 1999 十二月 1
事件Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea
持續時間: 1999 八月 221999 八月 25

Other

OtherProceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99
城市Seoul, South Korea
期間99-08-2299-08-25

指紋

Fuzzy Logic Controller
Linguistics
Fuzzy logic
Genetic algorithms
Genetic Algorithm
Controllers
Membership functions
Membership Function
Module
Shape Function
Concepts
Mathematical operators
Nonlinear systems
Simplify
Speedup
Nonlinear Systems
Hardware
Sufficient
Controller
Operator

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

引用此文

Chen, C. Y., Liu, B. D., & Tsao, J. Y. (1999). Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms. III-1299 - III-1304. 論文發表於 Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, .
Chen, Chuen Yau ; Liu, Bin Da ; Tsao, Ju Ying. / Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms. 論文發表於 Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, .
@conference{1a515f34689843b0bb8628c05e6dc91a,
title = "Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms",
abstract = "This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goal. The genetic algorithms search the optimal linguistic hedge combination in the linguistic hedge module. Accordingly, the linguistic hedge fuzzy logic controller has advantages: 1) it needs only the simple-shape membership functions for characterizing the related variables; 2) it is sufficient to adopt less number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design two well-known nonlinear systems. The simulation results demonstrate the effectiveness of this design.",
author = "Chen, {Chuen Yau} and Liu, {Bin Da} and Tsao, {Ju Ying}",
year = "1999",
month = "12",
day = "1",
language = "English",
pages = "III--1299 -- III--1304",
note = "Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 ; Conference date: 22-08-1999 Through 25-08-1999",

}

Chen, CY, Liu, BD & Tsao, JY 1999, 'Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms', 論文發表於 Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, 99-08-22 - 99-08-25 頁 III-1299 - III-1304.

Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms. / Chen, Chuen Yau; Liu, Bin Da; Tsao, Ju Ying.

1999. III-1299 - III-1304 論文發表於 Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, .

研究成果: Paper

TY - CONF

T1 - Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms

AU - Chen, Chuen Yau

AU - Liu, Bin Da

AU - Tsao, Ju Ying

PY - 1999/12/1

Y1 - 1999/12/1

N2 - This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goal. The genetic algorithms search the optimal linguistic hedge combination in the linguistic hedge module. Accordingly, the linguistic hedge fuzzy logic controller has advantages: 1) it needs only the simple-shape membership functions for characterizing the related variables; 2) it is sufficient to adopt less number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design two well-known nonlinear systems. The simulation results demonstrate the effectiveness of this design.

AB - This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goal. The genetic algorithms search the optimal linguistic hedge combination in the linguistic hedge module. Accordingly, the linguistic hedge fuzzy logic controller has advantages: 1) it needs only the simple-shape membership functions for characterizing the related variables; 2) it is sufficient to adopt less number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design two well-known nonlinear systems. The simulation results demonstrate the effectiveness of this design.

UR - http://www.scopus.com/inward/record.url?scp=0033281355&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033281355&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0033281355

SP - III-1299 - III-1304

ER -

Chen CY, Liu BD, Tsao JY. Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms. 1999. 論文發表於 Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, .