CONTENT DETERMINATION BY PSO-BASED LS-SVM REGRESSION

摘要：

Near infrared (NIR) spectroscopy has rapidly developed into an important and extremely effective analysis method. With the use of spectroscopy, support vector machine (SVM) was used as regressor. It is well known that the selection of hyper-parameters including the regularization and kernel parameters is important to the performance of least squares support vector machine (LS-SVM). In tbis paper, the particle swarm optimization (PSO) is applied to select the LS-SVM hyper-parameters. Additionally, to construct the learning samples, a spectrum energy-based approach is proposed to determine the wavelength region where the observed data are used to train LS-SVM for the regression task. Concentration prediction of water-ethanol mixtures is used to verify the proposed methods. Experimental results show that LS-SVM with RBF kernel is superior to conventional methods including artificial neural network and partial least squares models.

关键词： Least squares support vector machines Particle swarm optimization Parameter selection NIR Spectroscopy

作者: X.C.GUO Y.C.LIANG C.G.WU

作者单位: College of Science, Northeast Dianli University, Jilin 132012, P.R.China College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation a

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 1043-1047

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）