基于AR模型思想的高斯过程多模型建模方法

Gaussian process multi-model modeling method based on AR model

针对K-近邻算法中难以确定K值的定量问题,提出一种基于AR模型思想的高斯过程多模型建模方法。该方法借鉴AR模型的思想,将前一时刻的输出值作为当前时刻输出值的一个影响因素放入输入集中,通过计算训练样本的平均最小距离从而得到一个搜索半径,根据搜索半径来确定K值和K个近邻样本的权重,采用加权输出的方式以得到组合模型的输出。将其建模方法应用到某双酚A反应釜出口苯酚含量的软测量建模中,仿真结果表明,该方法具有较高的精度和较好的模型推广能力。

The value of K is difficult to be exactly determined in K-nearest neighbor algorithm.This paper proposed a Gaussian process multi-model modeling method based on the idea of AR model.The method introduced the model output value of previous moments into the input set of the current moment,calculated the mean minimum distance of the training samples to get a search radius.And it determined the value of K according to the radius and calculated the weights of the output according to the K neighbor samples.Finally it took the weighted output mode to get the output of combinational model.The method was used for the soft-sensor model to estimate the content of phenol at the outlet of a reaction vessel in a Bisphenol A production process.The simulation results show that the method has a higher accuracy and better model generalization ability.