贝叶斯分类器的关联向量机多模型软测量建模

Multiple model soft sensor with relevance vector machine based on Bayesian classifier

为了改善软测量模型的估计精度,提出了一种基于贝叶斯分类算法和关联向量机的多模型软测量建模方法。采用贝叶斯分类器对样本数据集进行分类,并对不同类别的输入数据分别建立关联向量回归机子模型,用"切换开关"方式组合作为最终的软测量模型输出。将该方法应用于双酚A生产过程的质量指标软测量建模,仿真结果表明:与单模型支持向量机相比,该方法估计精度较高,具有一定的应用价值。

In order to improve the estimation accuracy of the soft sensor model,a new nonlinear multi-modeling method based on Bayesian classify algorithm and relevance vector machine is proposed.This algorithm classifies the inputs by Bayesian classifier,and then trains each class by different relevance vector regression machines,and obtains the final result by the ＂Switch＂way.The proposed algorithm is used in a soft sensor model for the bisphenol-A productive process.The experimental results indicate the proposed algorithm is superior compared with the single model of SVM and has certain application value.