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Locally Weighted Prediction Methods for Latent Factor Analysis With Supervised and Semisupervised Process Data

机译:带有监督和半监督过程数据的潜在因子分析的局部加权预测方法

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摘要

Through calculating the similarity between the historical and the new query data samples, a probabilistic locally weighted prediction method based on supervised latent factor analysis (SLFA) model is proposed. In this method, the contributions of different historical samples are expressed through incorporating the similarity index into the noise variance of the process variables, which renders strong adaptability of the method for describing nonlinear relationships and abrupt changes of the process. Additionally, the proposed locally weighted method is extended to the semisupervised form, which is apparently more practical in real industrial processes, since the sampling rates of quality variables are much lower than those of ordinary process variables. Efficient expectation maximization algorithms are designed for parameter learning in both SLFA and semisupervised locally weighted LFA methods. Two real industrial processes are provided to evaluate the feasibility and the effectiveness of the newly developed soft sensors.
机译:通过计算历史查询数据与新查询数据样本之间的相似度,提出了一种基于监督潜在因子分析(SLFA)模型的概率局部加权预测方法。该方法通过将相似性指标纳入过程变量的噪声方差中来表达不同历史样本的贡献,这使得该方法用于描述非线性关系和过程的突然变化具有很强的适应性。此外,由于质量变量的采样率比普通过程变量的采样率低得多,因此建议的局部加权方法扩展为半监督形式,这在实际工业过程中显然更加实用。针对SLFA和半监督局部加权LFA方法中的参数学习,设计了有效的期望最大化算法。提供了两个实际的工业过程来评估新开发的软传感器的可行性和有效性。

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