基于Max-T模糊Hopfield网络的车型识别

Vehicle Pattern Recognition Based on Max-T Fuzzy Hopfield Network

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.