为克服支持向量机算法对噪声点和异常点的敏感性，采用清晰集合构造模糊集合法确定隶属度,采用混沌遗传算法优化参数的模糊最小二乘支持向量机分类器（FLS-SVMBCGA），并用著名的Ripley数据集、MONK数据集和PIMA数据集进行了数值实验，对油气输送管道的TPD检测信号进行了诊断.结果表明，FLS-SVMBCGA分类器能有效提高带噪声点和异常点数据集分类的预测精度，对油气输送管道的TPD信号分类效果高于91.67%，可实现对油气输送管道TPD信号的准确诊断。

In order to reduce the sensitivity of the support vector machines ( SVM) to noise and outliers,a new fuzzy least squares-support vector machines classifier based on chaos genetic algorithm is proposed and is abbreviated to FLS-SVMBCGA,in which the clear sets are used to construct a fuzzy membership set and the chaos genetic algorithm is adopted to optimize the parameters. Then,some experiments are carried out on three benchmarking datasets such as the Ripley dataset,the MONK dataset and the PIMA dataset. Finally,the TPD signals from oil and gas transmission pipeline are diagnosed using the proposed classifier. The results show that FLS-SVMBCGA is effective in improving the prediction accuracy of the classification problems with noises or outliers,with a classifying effect for TPD signals being higher than 91.67%,which means that the proposed algorithm can accurately diagnose the TPD signals from oil and gas transmission pipeline.