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一种基于LS-SVM的特征提取新方法及其在智能质量控制中的应用
引用本文:吴德会.一种基于LS-SVM的特征提取新方法及其在智能质量控制中的应用[J].计算机应用,2006,26(10):2446-2449.
作者姓名:吴德会
作者单位:合肥工业大学,九江学院
摘    要:提出一种基于最小二乘支持向量机(LS-SVM)的特征提取新方法,并将其成功应用到智能质量控制领域。首先,将线性特征提取公式表达成与LS-SVM回归算法中相同的形式;再遵循SVM方法将数据集由原输入空间映射到高维特征空间,进而使用该技巧通过线性形式实现非线性特征提取。然后,用常规控制图提取出一个含有6种模式、50维特征的仿真数据集用于测试,通过LS-SVM特征提取后,原数据集的特征被降到了3维并保留了原80%的分类信息。最后,用BP分类器对特征提取后的样本进行识别,其结果优于新型RSFM网络直接对原始样本进行识别的效果。仿真实验结果表明了LS-SVM特征提取方法的可行性和有效性。

关 键 词:最小二乘支持向量机    回归算法    特征提取    控制图    模式识别
文章编号:1001-9081(2006)10-2446-04
收稿时间:2006-04-24
修稿时间:2006-04-242006-06-09

Feature extraction method based on LS-SVM and its application to intelligent quality control
WU De-hui.Feature extraction method based on LS-SVM and its application to intelligent quality control[J].journal of Computer Applications,2006,26(10):2446-2449.
Authors:WU De-hui
Affiliation:1. Department of Electronic Engineering, Jiujiang University, Jiujiang Jiangxi 332005, China; 2. School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei Anhui 230009, China
Abstract:A new feature extraction method based on Least Squares Support Vector Machine (LS-SVM) was proposed and applied to intelligent quality control successfully. Firstly, the formulation of linear feature extraction was made in the same fashion as that in the LS-SVM linear regression algorithm. Secondly, the data was mapped from the original input space to a high dimensional one by following the usual SVM methodology so as that nonlinear feature extraction can be made from linear version of the formulation through applying the kernel trick. Thirdly, 50 dimensional simulated data sets, including six patterns, extracted by universal control chart, were used to test. As a result, characteristics of the original data sets declined to be 3 dimensional and 80% classification-messages remained. Finally, the BP-based abnormal pattern recognizer was applied to the characteristics extracted samples, and better results were obtained than that of directly recognizing original samples with RSFM methods. The simulation results indicate that this feature extraction method is not only feasible but also effective.
Keywords:Least Squares Support Vector Machine(LS-SVM)  regression algorithm  feature extraction  control chart  pattern recognition
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