Abstract Near-infrared (NIR) spectroscopy is frequently used to predict quality-relevant variables that are difficult to measure online. This technology can be applied by developing the NIR model in advance. Obtaining a high-accuracy NIR model is difficult using traditional modeling methods because process data inherently contain uncertainties and present strong non-Gaussian characteristics. Considering the difficulty in obtaining precise prediction results, biased estimation is important in producing qualified products when NIR spectroscopy is used in a feedback quality control system. The present work proposes a biased estimation model based on probabilistic representation to address the aforementioned issues. Additionally, a novel weighted incremental strategy with “just-in-time” learning is proposed to improve model adaptiveness. In this way, the NIR model could be established and maintained without imposing any distribution hypothesis on process data, and biased estimation could be obtained in the form of probability. The performance of the proposed method is demonstrated on an actual data set from a gasoline blending process.

中文翻译：

---

基于加权增量极小极大概率机的汽油调和过程质量预测方法

摘要 近红外 (NIR) 光谱经常用于预测难以在线测量的质量相关变量。这项技术可以通过提前开发 NIR 模型来应用。使用传统的建模方法很难获得高精度的 NIR 模型，因为过程数据固有地包含不确定性并呈现出强烈的非高斯特征。考虑到获得精确预测结果的困难，当 NIR 光谱用于反馈质量控制系统时，偏差估计对于生产合格产品很重要。目前的工作提出了一种基于概率表示的有偏估计模型来解决上述问题。此外，提出了一种具有“即时”学习的新型加权增量策略，以提高模型适应性。这样，无需对过程数据施加任何分布假设，即可建立和维护NIR模型，并以概率的形式获得有偏估计。所提出方法的性能在来自汽油混合过程的实际数据集上得到了证明。