복합형GLVQ 神經網을 利用한 車種分類 模型開發

서지정보

ㆍ발행기관 : 대한교통학회 ㆍ수록지정보 : 대한교통학회지 / 14권 / 4호
ㆍ저자명 : 趙亨基, 吳榮泰

목차

ABSTRACT
Ⅰ. 서론
Ⅱ. 이론적 고찰
Ⅲ. 차종구분 알고리즘 개발
Ⅳ. 차종인식실험
Ⅴ. 결론 및 향후과제
참고문헌

영어 초록

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithm, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog-Digital Converted data collected form ILD, this study used a typical and a modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method. As a result, the hybrid model of GLVQ neural network model is superior to the other methods.

참고 자료

없음