摘要
为提高智能交通系统中汽车车型自动识别的正确率,采用了多传感器移动车辆识别系统的数据融合算法,即用模糊神经网络建立数据融合决策模型,用BP算法对网络进行学习和训练,提高系统的精度和智能化.仿真实验结果表明了算法的有效性.
To improve the correct rate of automatic recognition of vehicle models by the Intelligent Transportation System,a data fusion algorithm for a multi-sensor recognition of moving vehicles is adopted.A data fusion decision-making model based on fuzzy neural networks is built,and learning and training on the network using BP algorithm is practiced to improve the accuracy and the intelligence of the system.Simulation experiment results show the algorithm is effective.
出处
《五邑大学学报（自然科学版）》
CAS
2010年第4期71-76,共6页
Journal of Wuyi University(Natural Science Edition)
基金
广东省科技计划项目(2007B01020034)
关键词
智能交通
移动车辆识别
数据融合
模糊神经网络
BP算法
intellectual traffic
recognition of moving vehicles
data fusion
fuzzy neural networks
BP algorithm
