Clustering of Vehicle waveform based on Principal Component Analysis and ART2 Neural Network

摘要：

Principal Component Analysis can reduce the dimension of data and eliminate the data correlation with retaining the most information. The dimension of vehicle waveform data was reduced by Principal Component Analysis and a new sample space was created. The new sample space which was produced by Principal Component Analysis is employed as the inputs of ART2 network. Hence, to the same recognition right-rate, the construction of ART2 network is simplified, and the convergent speed of the ART2 network is enhanced greatly due to the number of the ART2 inputs is reduced.

关键词： Principal Component Analysis ART2 Neural Network Vehicle Detection

作者: Shen Yanchao Ye Qing Lv wang

作者单位: Changsha University of Science&Technology, Changsha, Hunan, 410076, China

会议类型: 国际会议

会议名称: 2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 792-795

在线出版日期: 2010-03-13（万方平台首次上网日期，不代表论文的发表时间）