Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

被引：15

作者：

Cunkas, Mehmet ^{[1
]}

机构：

[1] Selcuk Univ, Dept Elect & Comp Educ, TR-42003 Konya, Turkey

来源：

JOURNAL OF INTELLIGENT MANUFACTURING | 2010年 / 21卷 / 04期

关键词：

Multiobjective fuzzy optimization; Genetic algorithms; Induction motor; OPTIMIZATION TECHNIQUES; EVOLUTIONARY ALGORITHM;

D O I：

10.1007/s10845-008-0187-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor's performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.

引用

页码：393 / 402

页数：10

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