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中文摘要：
提出了一种基于误差高斯混合模型（EGMM）的高斯过程回归（GPR）软测量方法.首先,选择合适的变量组成误差数据集,利用贝叶斯信息准则优化得到合适的高斯成分的个数;然后用EGMM对误差数据进行拟合计算得到条件误差均值和方差的表达式;最后当新的数据到来时,用建立的GPR模型进行输出预测,并利用EGMM模型得到的条件误差均值对输出进行补偿,从而得到更加精确的建模结果.通过数值仿真及硫回收装置（SRU）的H2S浓度的软测量,进一步验证所提算法的可行性和有效性.
英文摘要：
In this paper,we propose a Gaussian process regression（ GPR）-based error-Gaussian-mixture-model（ EGMM） soft sensor. First,we select appropriate variables to establish the error data and determine the optimal number of Gaussian components using a Bayesian information criterion. Next,we construct the EGMM based on the suitable error data to obtain the mathematical expressions for the conditional error mean and conditional error variance. When a new sample is available,the constructed GPR model can be used for output prediction. Then the conditional error mean of the new sample is computed using the EGMM model to compensate the prediction output in order to achieve a more accurate prediction. We performed a numerical simulation and soft sensor prediction of the H2 S concentrations of a sulfur recovery unit（ SRU）,and the results demonstrate the feasibility and effectiveness of the proposed approach.
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