针对最小二乘支持向量机( LS-SVM)在建立醋酸乙烯(VAC)聚合率软测量模型过程中最优模型参数的选择问题,提出了利用一种量子遗传算法来自动选取LS-SVM模型正则化参数和核函数参数的方法；把LS-SVM模型参数的选择问题转化为优化问题,利用全局搜索能力强的量子遗传算法优化LS-SVM建模过程的重要参数,建立了基于QGA-LSSVM方法的VAC聚合率软测量模型；仿真结果表明:与已有的神经网络和支持向量机软测量方法相比,该模型泛化能力强,精度高,更有利于醋酸乙烯聚合率测量工程实际运用.%An quantum genetic algorithm (QGA) was proposed to overcome the disadvantage that it' s difficult to get better parameter values of least squares support vector machine (LS-SVM) and the mixed kernel function in the processing of establish the soft sensing of vinyl acetate CVAC) polymerization rate. The method can convert the LS-SVM model parameters of selection into optimization problem, the best parameters of LS-SVM would be selected by QGA which has the ability of better search, and the QGA-LSSVM mode about soft sensing of VAC polymerization rate was constructed. The simulation result indicated that compared with the methods based on neural network and support vector machine, the QGA-LSSVM model has more .effective generation performance and high precision, and it is more conducive to the practical application of engineering measurements.
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