Title :
Interharmonic Detection Based on Support Vector Machine
Author :
Li, ZHOU ; Kaipei, Liu ; Bingwei, MA ; Qian, Tao
Author_Institution :
Sch. of Electr. Eng., Wuhan Univ.
Abstract :
Support vector machine (SVM) based on the principle of structure risk minimization provides a new perspective in machine learning, and has been successfully applied to many areas in the last years, especially for pattern recognition and function fitting. In this paper, because of the adjustable resolution of the SVM algorithm, a new way to measure interharmonic is put forward, namely using wide analytical domain of frequency with low resolution at first and then using narrow analytical domain of frequency with high resolution to obtain the frequency spectrum of the signal. And based on this new method, the interharmonic detection system is designed and implemented with the prevailing and powerful software platform of LabVIEW with graphical nature
Keywords :
frequency-domain analysis; learning (artificial intelligence); minimisation; pattern recognition; support vector machines; LabVIEW; frequency spectrum; function fitting; interharmonic detection; machine learning; pattern recognition; risk minimization; software platform; support vector machine; Algorithm design and analysis; Frequency domain analysis; Frequency measurement; Machine learning; Machine learning algorithms; Pattern recognition; Risk management; Signal analysis; Signal resolution; Support vector machines; LabVIEW; Support vector machine; interharmonic;
Conference_Titel :
Industrial Electronics and Applications, 2006 1ST IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-9513-1
Electronic_ISBN :
0-7803-9514-X
DOI :
10.1109/ICIEA.2006.257157
