中文摘要：

以LS-SVM算法为基础,建立了权重可优化的多因变量LS-SVM回归模型,给出了相应的算法（MLS-SVM）,并从理论上说明了它与LS-SVM的关系。以64个高粱样品为实验材料,其中建模集与预测集中样品的比例为51∶13。从区间[0,1]之间均匀地随机选取5组权重,根据预测平均相对误差最小的准则,按照LOO方式确定了一组合适的权重及参数,建立了近红外光谱同时分析三个化学组分蛋白质、赖氨酸和淀粉的多因变量定量分析模型。结果得到三个组分模型的预测值与实际值的平均相对误差分别为1.65%,6.47%和1.37%,相关系数分别为0.9940,0.83920.8825,而LS-SVM算法建模预测三个组分的平均相对误差分别为1.68%,6.25%和1.47%,相关系数分别为0.9941,0.8310和0.8800。可见MLS-SVM算法与LS-SVM算法的建模分析效果相当,且都取得了较满意的结果,验证了MLS-SVM算法同时定量分析多组分含量的可行性。另外,文章也验证了不同权重对MLS-SVM算法的预测性能有一定影响,由此表明在实际多因变量建模分析中对权重进行优化是必要的。

英文摘要：

In the present paper,on the basis of LS-SVM algorithm,we built a multiple dependent variables LS-SVM（MLS-SVM） regression model whose weights can be optimized,and gave the corresponding algorithm.Furthermore,we theoretically explained the relationship between MLS-SVM and LS-SVM.Sixty four broomcorn samples were taken as experimental material,and the sample ratio of modeling set to predicting set was 51∶13.We first selected randomly and uniformly five weight groups in the interval,and then in the way of leave-one-out（LOO） rule determined one appropriate weight group and parameters including penalizing parameters and kernel parameters in the model according to the criterion of the minimum of average relative error.Then a multiple dependent variables quantitative analysis model was built with NIR spectrum and simultaneously analyzed three chemical constituents containing protein,lysine and starch.Finally,the average relative errors between actual values and predicted ones by the model of three components for the predicting set were 1.65%,6.47% and 1.37%,respectively,and the correlation coefficients were 0.994 0,0.839 2 and 0.882 5,respectively.For comparison,LS-SVM was also utilized,for which the average relative errors were 1.68%,6.25% and 1.47%,respectively,and the correlation coefficients were 0.994 1,0.831 0 and 0.880 0,respectively.It is obvious that MLS-SVM algorithm is comparable to LS-SVM algorithm in modeling analysis performance,and both of them can give satisfying results. The result shows that the model with MLS-SVM algorithm is capable of doing multi-components NIR quantitative analysis synchronously.Thus MLS-SVM algorithm offers a new multiple dependent variables quantitative analysis approach for chemometrics.In addition,the weights have certain effect on the prediction performance of the model with MLS-SVM,which is consistent with our intuition and is validated in this study.Therefore,it is necessary to optimize weights in multiple dependent variables NIR modeling analysis.