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首页> 外文期刊>IEEE transactions on industrial informatics >Nonlinear Gaussian Mixture Regression for Multimode Quality Prediction With Partially Labeled Data
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Nonlinear Gaussian Mixture Regression for Multimode Quality Prediction With Partially Labeled Data

机译：带有部分标记数据的多模质量预测的非线性高斯混合回归

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摘要

An enhanced nonlinear Gaussian mixture regression (NLGMR) algorithm is proposed for quality prediction of a nonlinear multimode process. The traditional Gaussian mixture regression (GMR) model has been utilized for quality prediction with a linear model in each local mode, which will not suit for many cases that nonlinear relationships exist between input and output variables. Besides, large scales of process data that can be used for modeling are partially labeled on account of the low sampling rate of quality variables. Most of the unlabeled samples are discarded while building the GMR model, which leads to the loss of information and limits the improvement of prediction accuracy. To tackle these two problems, a locally weighted semisupervised factor analysis model is developed in each mode of GMR. The locally weighted model divides the nonlinear process into pieces of linear model and the semisupervised factor analysis model can effectively take advantage of the massive unlabeled data. Moreover, the variational inference (VI) algorithm is conducted on theGMR model to determine the amount of process modes automatically. The proposed method is first verified by a numerical example and then applied in a multimode primary reformer to predict the oxygen content, where prominent improvements are obtained, compared with traditional methods.
机译：提出了一种改进的非线性高斯混合回归（NLGMR）算法，用于非线性多模过程的质量预测。传统的高斯混合回归（GMR）模型已用于在每个局部模式下使用线性模型进行质量预测，这不适用于许多情况下输入和输出变量之间存在非线性关系。此外，由于质量变量的采样率低，可部分标记可用于建模的大量过程数据。在构建GMR模型时，大多数未标记的样本都将被丢弃，这会导致信息丢失并限制预测准确性的提高。为了解决这两个问题，在每种GMR模式下都开发了局部加权半监督因素分析模型。局部加权模型将非线性过程分为线性模型，半监督因素分析模型可以有效利用大量未标记数据。此外，对GMR模型进行了变分推理（VI）算法，以自动确定过程模式的数量。与传统方法相比，该方法首先通过数值示例进行了验证，然后应用于多模式一次重整器中以预测氧含量，并获得了明显的改进。

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