### 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 22 → 1999 八月 25 |

### Other

Other | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 |
---|---|

城市 | Seoul, South Korea |

期間 | 99-08-22 → 99-08-25 |

### 指紋

### All Science Journal Classification (ASJC) codes

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

### 引用此文

*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, .

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

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.

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SP - III-1299 - III-1304

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