基于Max-T模糊Hopfield网络的车型识别  

Vehicle Pattern Recognition Based on Max-T Fuzzy Hopfield Network

作　　者：宋丹 周刚 

机构地区：[1]武汉市工程科学技术研究院,湖北武汉430000

出　　处：《计算技术与自动化》2014年第2期105-107,共3页Computing Technology and Automation

基　　金：科技部863计划项目(2012AA012904)

摘　　要：在许多模式识别采样的过程中,由于环境噪声和设备误差,往往导致采集的数据与真实值有一定偏差,这种偏差会影响识别的效果。本文采用Max-T FHNN模型,提出一种应用于智能化交通管理的车型识别方法。并用实验证实相对于其他车型识别方法,该方法在待测样本含有噪声的情况下能得到更好的识别率。During the time collecting samples in many pattern recognitions,because of the environmental noise and the precision of equipment impacts,the data that have been collected always have some perturbations,the perturbations have disadvantages to the effect of pattern recognition system.The practical applications of the Max-T FHNN model have been studied in this thesis.Using Max-T FHNN and other pattern recognition techniques,a vehicle recognition method for intelligent traffic management is proposed.Experiments prove that compared to other method,the recognition rate is better when the method is used under noise environment.

关 键 词：模糊HOPFIELD神经网络 学习算法 Max-T 模式识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]