模糊支持向量机在路面识别中的应用

利用模糊支持向量机进行路面不平度识别。针对支持向量机对样本中的噪声点和野值点特别敏感的缺点，采用将样本到类中心的距离作为样本的模糊隶属度，并结合改进的粒子群算法对模糊支持向量机的参数进行优化。通过对实验数据的训练和测试，该方法的最高平均识别率提高到了77.5%，高于一般支持向量机的72.5%的识别率。数据处理表明模糊隶属度的引入强化了有效样本对分类的影响，减弱了噪声点和野值点对分类的影响，提高了路面不平度识别率。

Application of Fuzzy Support Vector Machine on Road Type Recognition

Fuzzy support vector machine (FSVM) is used on road roughness recognition. The general SVM is particularly sensitive to the noise points and outliers in the samples, so a method is proposed, in which the distance from sample to the center of class is taken as the fuzzy membership of the sample and the parameters of FSVM are optimized by improved particle swarm optimization (PSO) algorithm. After training and testing the experimental data, the highest average recognition rate increases to 77.5%, which is higher than 72.5% that of the method with the general support vector machine. Data processing indicates that FSVM strengthens the influence of effective samples on classification and weaken influence of noise points and outliers. Furthermore, the recognition rate of road roughness has been improved.