Clustering of Vehicle Waveform Based on Principal Component Analysis and ART2 Neural Network

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.