|本期目录/Table of Contents|

[1]许爱德,樊印海,李自强,等.基于GA-ANFIS的开关磁阻电机建模[J].电机与控制学报,2011,(07):54-59.
 XU Ai-de,FAN Yin-hai,LI Zi-qiang.Modeling of switched reluctance motor based on GA-ANFIS[J].,2011,(07):54-59.
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基于GA-ANFIS的开关磁阻电机建模(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2011年07
页码:
54-59
栏目:
出版日期:
2011-07-20

文章信息/Info

Title:
Modeling of switched reluctance motor based on GA-ANFIS
作者:
许爱德; 樊印海; 李自强;
大连海事大学信息科学技术学院;
Author(s):
XU Ai-deFAN Yin-haiLI Zi-qiang
College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China
关键词:
开关磁阻电机 建模 自适应网络模糊推理系统 遗传算法
Keywords:
switched reluctance motors modeling adaptive network based fuzzy inference system genetic algorithms
分类号:
TM 352
DOI:
-
文献标志码:
A
摘要:
针对在使用自适应神经网络模糊推理系统对开关磁阻电机进行建模的过程中收敛速度慢的问题,采用将遗传算法和自适应神经网络模糊推理系统相结合的开关磁阻电机建模方法。网络结构仍然采用具有很强鲁棒性和自适应性的Takagi-Sugeno模型,而在网络参数训练时将遗传算法与自适应神经网络模糊推理系统的传统混合学习算法相结合,以提高训练速度。根据实测的8/6极开关磁阻电机的样本数据,对开关磁阻电机的电感和转矩进行建模,仿真结果表明,该方法具有很高的精确度和很强的泛化能力,并且将收敛速度提高了两倍多。将所建模型应用到开关磁阻电机控制系统仿真中,并与实际控制系统进行对比,两者结果基本一致,证明了该方法的正确性和可行性。
Abstract:
To improve the convergence speed of modeling for switched reluctance motor(SRM) using adaptive network based fuzzy inference system(ANFIS),a mathematic model for SRM was proposed.This method combined the genetic algorithms(GA) with ANFIS.The fuzzy neural networks adopted Takagi-Sugeno model which has strong robustness and adaptability.GA were introduced to traditional hybrid algorithm of ANFIS to improve the training speed during parameter training.Based on testing sample data of 8/6 SRM,the ind...

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备注/Memo

备注/Memo:
国家经贸委“十一五”重大技术装备电力推进技术研制项目(JDRo-Z4-01)
更新日期/Last Update: 2012-03-14