支持向量机理论及算法研究综述

介绍了SVM的理论基础和它的多种主要算法及这些算法的利弊与发展现状,并介绍了SVM在现实生活中的应用原理及应用现状。最后分析了SVM在发展中的不足之处,指出了其研究方向及前景,并提出在分布式支持向量机这个方向上可以进行更深层次的研究。

Dual membership SVM method based on spectral clustering

A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is pro- posed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of ＂overlapping＂ region between the two training classes. The proposed method handles sample ＂overlap＂ effi- ciently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.

飞机电动液压泵寿命分布智能识别方法研究

研究飞机电动液压泵寿命分布类型,有助于发现可靠性的薄弱环节,在系统出现故障之前进行排故和检修,保证性能和可靠性水平不断提高。为提高飞机电动液压泵寿命分布识别精度,需要对寿命数据提取较多的数字特征作为依据,运用传统方法对其进行处理时,计算复杂,容易产生过学习现象;且飞机电动液压泵寿命分布对应的可靠性分布较多,传统方法易产生错分和不可分现象,难以达到较高的精度要求。针对以上问题,提出了一种结合核主元分析和模糊支持向量机的可靠性寿命分布识别方法。在核主元分析良好的降维能力基础上,充分利用模糊支持向量机优良的分类性能,建立了高效的KPCA-FSVM寿命分布识别模型,并将其应用于电动液压泵系统寿命分布识别。仿真结果表明,改进模型能够降低计算的复杂度,准确识别出电动液压泵寿命。