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首页> 外文期刊>AIChE Journal >A Bayesian Framework for Real-Time Identification of Locally Weighted Partial Least Squares
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A Bayesian Framework for Real-Time Identification of Locally Weighted Partial Least Squares

机译：用于局部加权局部最小二乘实时识别的贝叶斯框架

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摘要

Just-in-time (JIT) learning methods are widely used in dealing with nonlinear and multimode behavior of industrial processes. The locally weighted partial least squares (LW-PLS) method is among the most commonly used JIT methods. The performance of LW-PLS model depends on parameters of the similarity function as well as the structure and parameters of the local PLS model. However, the regular LW-PLS algorithm assumes that the parameters of the similarity function and structure of the local PLS model are known and do not fully utilize available knowledge to estimate the model parameters. A Bayesian framework is proposed to provide a systematic way for real-time parameterization of the similarity function, selection of the local PLS model structure, and estimation of the corresponding model parameters. By applying the Bayes' theorem, the proposed framework incorporates the prior knowledge into the identification process and takes into account the different contribution of measurement noises. Furthermore, Bayesian model structure selection can automatically deal with the model complexity problem to avoid the overfitting issue. The advantages of this new approach are highlighted through two case studies based on the real-world near infrared data. (c) 2014 American Institute of Chemical Engineers AIChE J, 61: 518-529, 2015
机译：即时（JIT）学习方法被广泛用于处理工业过程的非线性和多模式行为。局部加权偏最小二乘（LW-PLS）方法是最常用的JIT方法之一。 LW-PLS模型的性能取决于相似性函数的参数以及局部PLS模型的结构和参数。但是，常规的LW-PLS算法假定相似函数的参数和局部PLS模型的结构是已知的，并且没有充分利用可用的知识来估计模型参数。提出了一种贝叶斯框架，为相似性函数的实时参数化，局部PLS模型结构的选择以及相应模型参数的估计提供一种系统的方法。通过应用贝叶斯定理，提出的框架将先验知识纳入了识别过程，并考虑了测量噪声的不同贡献。此外，贝叶斯模型结构选择可以自动处理模型复杂性问题，从而避免过拟合问题。通过基于真实世界近红外数据的两个案例研究，强调了这种新方法的优势。 （c）2014美国化学工程师学会AIChE J，61：518-529，2015

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