Application of LVQ neural network in traffic incident detection

doi:10.3778/j.issn.1002-8331.2008.34.065

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (34): 213-215.DOI: 10.3778/j.issn.1002-8331.2008.34.065

• 工程与应用 • Previous Articles Next Articles

Application of LVQ neural network in traffic incident detection

ZHU Hong-bin

College of Computer and Information Engineering，Lishui University，Lishui，Zhejiang 323000，China

Received:2007-08-20 Revised:2008-02-14 Online:2008-12-01 Published:2008-12-01
Contact: ZHU Hong-bin

LVQ神经网络在交通事件检测中的应用

朱红斌

丽水学院计算机与信息工程学院，浙江丽水 323000

通讯作者: 朱红斌

Abstract

Abstract: A novel method is proposed for traffic incidents detection based on LVQ neural network.The features of flow and occupancy rate are extracted from traffic incidents.Then LVQ neural network is used to classify the traffic incidents.LVQ has a simple network structure，but it is very effective and robust in traffic incidents detection.In order to improve the precision of the LVQ neural network for traffic incidents detection，Boosting algorithm is used to build an integration-neural network.Finally the simulation with Matlab shows the algorithm can get better performance.

Key words: Boosting algorithm, LVQ neural network, classify, traffic incident detection

摘要： 提出一种基于LVQ神经网络的交通事件检测方法。提取上下游的流量和占有率为特征，LVQ神经网络作为分类器进行交通事件自动检测。LVQ网络结构简单，但却表现出比BP神经网络更强的有效性和鲁棒性。为进一步提高神经网络的泛化能力，采用改进的Boosting算法，进行网络集成。运用Matlab 进行了仿真分析，结果表明提出的交通事件检测算法具有良好的检测性能。

关键词: Boosting算法, LVQ神经网络, 分类器, 交通事件检测

ZHU Hong-bin. Application of LVQ neural network in traffic incident detection[J]. Computer Engineering and Applications, 2008, 44(34): 213-215.

朱红斌. LVQ神经网络在交通事件检测中的应用[J]. 计算机工程与应用, 2008, 44(34): 213-215.

[1]	WEI Jiamin1, FENG Jun1, BU Qirong1, GAO Yuan2, ZHAO Yan1. Off-line Chinese handwriting signature verification with hierarchical classification [J]. Computer Engineering and Applications, 2018, 54(8): 112-118.
[2]	YAN Jianhong. Optimization boosting classification based on metrics of imbalanced data [J]. Computer Engineering and Applications, 2018, 54(21): 128-132.
[3]	ZHANG Peng. Framework for similarity detection among Android applications based on property classifying [J]. Computer Engineering and Applications, 2018, 54(17): 10-15.
[4]	XUE Zhengyuan. Improved probability-based Bayesian anti-spam mechanism [J]. Computer Engineering and Applications, 2013, 49(7): 98-101.
[5]	YANG Weichen, LING Haifeng, WU Peng, XU Weiya. Ontology retrieve matching algorithm based on similarity matrix body [J]. Computer Engineering and Applications, 2013, 49(22): 127-131.
[6]	CHEN Shaowei, WANG Cong, ZHAO Shuai. Application of decision tree in circuit fault diagnosis [J]. Computer Engineering and Applications, 2013, 49(12): 233-236.
[7]	QIAO Jian，TIAN Qing. Fast classify multi-label data by using information of nearest neighbors [J]. Computer Engineering and Applications, 2011, 47(32): 138-140.
[8]	BI Xia’an，ZHANG Dafang，ZHAO Jiaojiao. Efficient technology for identifying and classifying game traffic [J]. Computer Engineering and Applications, 2011, 47(23): 101-103.
[9]	HUANG Jun，QIN Feng，CHENG Zekai，YANG Fan. Weights-based accuracy evaluation method for multi-label classifier [J]. Computer Engineering and Applications, 2011, 47(10): 135-137.
[10]	GUO Zi-xue^1，2，ZHANG Qiang². Application of fuzzy clustering analysis in classification of emergency materials [J]. Computer Engineering and Applications, 2009, 45(35): 208-211.
[11]	PAN Jun，LI Hong，LI Bo. Research on classification algorithms in imbalanced data based on boosting [J]. Computer Engineering and Applications, 2009, 45(25): 138-140.
[12]	LUO Jun-li，HUANG Guang-jun，SUN Jian-guo. Efficient algorithm for ontology matching [J]. Computer Engineering and Applications, 2009, 45(21): 94-96.
[13]	SHEN Shao-hao,XIA Li-min,ZHANG Liang-chun. Gait recognition based on adaptive feature selecting [J]. Computer Engineering and Applications, 2008, 44(7): 199-201.
[14]	WANG Kai,ZHANG Yong-xiang,YAO Xiao-shan,LI Jun. Adaptive adjust method for penalization parameter of support vector machines [J]. Computer Engineering and Applications, 2008, 44(26): 45-47.
[15]	LIU Quan-jin¹,LI Ying-xin². Application of Boosting algorithm to sample categorization of gene expression profiles [J]. Computer Engineering and Applications, 2008, 44(14): 228-230.

Application of LVQ neural network in traffic incident detection

LVQ神经网络在交通事件检测中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics