文章基本信息

标题：VEHICLES CLASSIFICATION BASED ON DIFFERENT COMBINATION OF FEATURE EXTRACTION ALGORITHM WITH NEURAL NETWORK (NN) USING FORWARD SCATTERING RADAR (FSR)
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作者：NUR FADHILAH ABDULLAH ; NUR EMILEEN ABD RASHID ; ISMAIL MUSIRIN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：77
期号：3
出版社：Journal of Theoretical and Applied
摘要：Feature extraction process plays an important role in classifying radar target. The extracted features will be fed as the input to the classifier. The incorrect choice of extracted features will cause poor performance of radar classification system. This paper presents the vehicles classification based on combination of different feature extraction algorithm with neural network using forward scattering radar. Hence, the main objective of this paper is to analyze the most suitable feature extraction algorithm which based on different dimensionality reduction technique in order to evaluate the performance of classification system. Three different techniques are used such as the manual and automatic reduction technique (PCA and Z-score) and neural network as classifier to classify the vehicles into their groups either medium or large based on their physical size. The performance of the classification system is determined by the percentage of classification accuracy. The higher percentage of the classification accuracy shows the better performance of the classification system.
关键词：Neural Network (NN); Forward Scatter Radar (FSR); Principal Component Analysis (PCA); Z-score;Classification Accuracy (CA)