论文标题
具有基于SVM的开关逻辑的多模型线性推理传感器的设计
Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic
论文作者
论文摘要
我们研究了基于数据的多模型线性推理（软）传感器的设计问题。多模型线性推论传感器有望提高预测准确性，但模型结构和训练的简单性。多模型推断传感器设计的标准方法包括三个单独的步骤：1）数据标记（建立单个模型的培训子集），2）数据分类（为模型创建切换逻辑），以及3）培训单个模型。这个概念有两个主要问题：a）作为步骤2）和3）是单独的，在模型之间切换时可能会发生不连续性； b）作为步骤1）和3）是独立的，数据标记无视所得模型的质量。我们的贡献旨在提到这两个问题，在该问题中，对于问题a），我们引入了一种基于SVM的新型模型训练，并结合切换逻辑识别，以及问题b），我们建议对数据标记进行直接优化。我们在化学工程领域的一个例子中说明了提出的方法及其好处。
We study the problem of data-based design of multi-model linear inferential (soft) sensors. The multi-model linear inferential sensors promise increased prediction accuracy yet simplicity of the model structure and training. The standard approach to the multi-model inferential sensor design consists in three separate steps: 1) data labeling (establishing training subsets for individual models), 2) data classification (creating a switching logic for the models), and 3) training of individual models. There are two main issues with this concept: a) as steps 2) & 3) are separate, discontinuities can occur when switching between the models; b) as steps 1) & 3) are separate, data labelling disregards the quality of the resulting model. Our contribution aims at both the mentioned problems, where, for the problem a), we introduce a novel SVM-based model training coupled with switching logic identification and, for the problem b), we propose a direct optimization of data labelling. We illustrate the proposed methodology and its benefits on an example from the chemical engineering domain.
