多属性核函数快速向量机的污水在线软测量

Wastewater Treatment Plant Based on Multi-attribute Kernel Fast Vector Machine

针对污水生化处理过程复杂、在线仪表的维护困难等问题,提出了一种基于多属性高斯核函数的快速向量机在线污水软测量模型.该模型通过多属性高斯核来构造快速相关向量机的贝叶斯矩阵,通过引入快速边际似然算法来加快迭代更新的速度.将所提算法与支持向量机(SVM)、相关向量机(RVM)、快速相关向量机(FASTRVM)及几种基于不同核函数的快速相关向量机算法进行对比实验,结果表明所提方法可减小相关向量个数,提高预测精度,尤其可显著减少软测量建模的计算量.实验结果证明了该方法在污水系统在线软测量的有效性.

Given the complexity of the wastewater treatment process and the difficulties in online instrument maintenance,soft measurement with the use of a computer has become a valid way to evaluate the performance of the wastewater treatment process. We propose a novel online soft measuring model based on multi-attribute Gaussian kernel function FAST relevance vector machine( MAG-FASTRVM). This novel model establishes a Bayesian matrix with MAG kernel functions and accelerates the update speed with fast marginal likelihood algorithm.Experiment results verify that the proposed model can reduce the number of relevance vectors and improve prediction accuracy compared with the support vector machine,the relevance vector machine,the FASTRVM,and several multi-kernel function FASTRVMs. The computation time of modeling is also significantly reduced. The proposed model is effective for online soft measurement in the wastewater treatment process.