会议专题

A Novel Intelligent System Based on WPA-SVMs for Fault Signals Recognition

Conventional wavelet transform (WT) omits some useful details information of fault signals since it only decomposes low frequency band in a higher scale. In this paper, a novel intelligent system is presented for real-time detection and diagnosis of the fault signals. The model consists of wavelet packet analysis (WPA) unit and support vector machines (SVMs) unit. When signals are decomposed in wavelet packet space, different frequency bands are processed from original signals adequately. WPA can improve abilities of feature extraction than conventional WT. We use a large number of samples to compare the accuracy rate of three kinds kernel function of SVMs, the results indicate that accuracy of Gaussian kernel is higher than polynomial kernel and multilayer perceptron (MLP) kernel. No matter whether the data set is small or huge, accurate classification rate of SVMs is better than RBF and BP neural network methods for normal and exceptional subjects.

wavelet transform wavelet packet analysis support vector machine pattern recognition

Xian Guang-Lin Zhang yu Liu Lie-gen Xian Guang-ming

Research Institute of Computer Application,Guangzhou 510640, China School of Computer Science, South China University of Technology,Guangzhou 510640, China

国际会议

2005年无线通信、网络和移动计算国际会议

武汉

英文

591-594

2005-09-23(万方平台首次上网日期,不代表论文的发表时间)