基于自适应增强算法的支持向量机组合模型

为了提高软测量模型的泛化能力,提出一种基于AdaBoosting算法的组合支持向量机(SVM)模型.该方法在贝叶斯分析的基础上,利用样本概率初始化惩罚系数,依据回归过程中的损失函数更新惩罚系数权重,使得SVM训练模型有强、弱之分,突出一些重要样本的作用,以提高模型的估计精度和泛化能力.仿真结果表明,依据该方法建立的组合模型明显改善了软测量模型的估计能力和泛化能力.

A Compositional Model of SVM based on AdaBoosting Algorithm

In order to improve the generalization ability of a soft-sensor model, a compositional model of SVM based on
AdaBoosting algorithm is proposed. On the basis of Bayesian analysis, the penalty coefficient is initialized by using the Bayesian probability of the samples, and then the penalty weight is updated by the loss function in the regression process so that the SVM training model can highlight some important samples to improve its estimation accuracy and generalization ability. Simulation result shows that this approach can greatly improve the estimation capacity and generalization ability of the model.