Title :
Clustering of Vehicle Waveform Based on Principal Component Analysis and ART2 Neural Network
Author :
Yanchao Shen ; Qing Ye ; Wang Lv
Author_Institution :
Changsha Univ. of Sci. & Technol., Changsha, China
Abstract :
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.
Keywords :
ART neural nets; principal component analysis; traffic engineering computing; waveform analysis; ART2 network; data correlation elimination; data dimension reduction; principal component analysis; sample space; vehicle waveform clustering; Coils; Eddy currents; Electromagnetic induction; Frequency; Induction generators; Insulation; Magnetic fields; Neural networks; Principal component analysis; Vehicle detection; ART2 NeuralNetwork; Principal Component Analysis; Vehicle Detection;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.776
