An Intelligent Decision Support Tool for a Travelling Wave Ultrasonic Motor Based on k-Nearest Neighbor Algorithm

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI:10.1109/ICMLA.2011.33

Ş. Sağiroğlu, H. Kahraman, M. Yesilbudak, I. Colak

{"title":"An Intelligent Decision Support Tool for a Travelling Wave Ultrasonic Motor Based on k-Nearest Neighbor Algorithm","authors":"Ş. Sağiroğlu, H. Kahraman, M. Yesilbudak, I. Colak","doi":"10.1109/ICMLA.2011.33","DOIUrl":null,"url":null,"abstract":"Driving frequency, amplitude and phase difference of two-phase sinusoidal voltages are the input parameters which have influence on speed stability of travelling wave ultrasonic motors (TWUSMs).These parameters are also time-varying due to the variations in operating temperature. In addition, a complete mathematical model of the TWUSM has not been derived yet. Owing to these reasons, a machine learning approach is required for determining the compatibility of operating parameters related to speed stability of TWUSMs. For this purpose, an intelligent decision support tool has been designed for TWUSMs in this study. The input parameters such as driving frequency, amplitude, phase difference of two-phase sinusoidal voltages and operating temperature were evaluated by the k-nearest neighbor algorithm in the decision support tool. The results have shown that the proposed tool provides effective results in the compatibility determination of operating parameters related to speed stability of TWUSMs.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 10th International Conference on Machine Learning and Applications and Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2011.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Driving frequency, amplitude and phase difference of two-phase sinusoidal voltages are the input parameters which have influence on speed stability of travelling wave ultrasonic motors (TWUSMs).These parameters are also time-varying due to the variations in operating temperature. In addition, a complete mathematical model of the TWUSM has not been derived yet. Owing to these reasons, a machine learning approach is required for determining the compatibility of operating parameters related to speed stability of TWUSMs. For this purpose, an intelligent decision support tool has been designed for TWUSMs in this study. The input parameters such as driving frequency, amplitude, phase difference of two-phase sinusoidal voltages and operating temperature were evaluated by the k-nearest neighbor algorithm in the decision support tool. The results have shown that the proposed tool provides effective results in the compatibility determination of operating parameters related to speed stability of TWUSMs.

查看原文本刊更多论文

基于k-最近邻算法的行波超声电机智能决策支持工具

两相正弦电压的驱动频率、幅值和相位差是影响行波超声电机速度稳定性的输入参数。由于工作温度的变化，这些参数也随时间变化。此外，TWUSM的完整数学模型尚未导出。由于这些原因，需要一种机器学习方法来确定与twusm速度稳定性相关的操作参数的兼容性。为此，本研究为twusm设计了智能决策支持工具。采用决策支持工具中的k近邻算法对驱动频率、幅值、两相正弦电压相位差和工作温度等输入参数进行评估。结果表明，该工具在TWUSMs速度稳定性相关操作参数的相容性测定中提供了有效的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2011 10th International Conference on Machine Learning and Applications and Workshops

自引率

0.00%

发文量