将其应用于模糊支持向量机方法中，较好地将支持向量与含噪声或野值样本区分开。
The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises.
仿真试验结果表明这种新的模糊支持向量机方法不但有较高的分类准确率，而且对隶属度有很强的预测能力。
Emulational experimental result shows that this new fuzzy support vector machine method not only has higher classified accuracy, but also has stronger test capability for the membership degree.
提出支持向量机-模糊预测控制方法，介绍支持向量机在列车启动控制过程中的应用。
It is proposed a fuzzy forecast control method based on support vector machine. The applications of the machine to the train start-up control are given.
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