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Transfer learning based on incorporating source knowledge using Gaussian process models for quick modeling of dynamic target processes

机译：基于结合源知识的基于使用高斯进程模型来转移学习，以便快速建模动态目标过程

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摘要

To maintain optimum economic process performance, a good process model is the cornerstone of an optimal scheduling strategy and controller design. Up to now, approaches to dynamic modeling have already been studied, but the models they constructed are only valid in their corresponding operating conditions. As operating conditions switch fast during the production, the constructed model may lack the extrapolating capability and may not describe the process behaviors in the new operating condition properly. Especially in the case that only a small number of data can be collected from the new operating condition for the construction of the model; the performance of the model may not be guaranteed for online new data. In this paper, a dynamic transfer modeling approach based on the Gaussian process model (GPM) is proposed. It can quickly model the target process and get correct predictions, by transferring source model knowledge trained with a sufficient number of historical data to a target model with a small number of available target data. This can significantly reduce the amount of time waiting for getting the target process data and quickly achieve a good process model. The statistical approach leverages GPM to transfer the knowledge. GPM is introduced to capture the uncertainty that propagates from the source process to the target process. Thus, the multi-step ahead prediction of the target model can provide the mean prediction as well as probabilistic information for its prediction in the form of a predictive variance. Finally, CSTR and the real furnace system are used to demonstrate the features of the proposed method and the applicability to a real plant process.
机译：为了保持最佳的经济流程性能，良好的过程模型是最佳调度策略和控制器设计的基石。到目前为止，已经研究了动态建模的方法，但它们所构造的模型仅适用于它们相应的操作条件。由于操作条件在生产过程中快速切换，所构造的模型可能缺乏外推能力，并且可能无法正确描述新的操作条件下的过程行为。特别是在只能从新的操作条件中收集少量数据的情况下，以便建造模型;在线新数据可能无法保证模型的性能。本文提出了一种基于高斯过程模型（GPM）的动态传递建模方法。它可以通过将具有足够数量的历史数据训练的源模型知识传输到具有少量可用目标数据的目标模型来快速模拟目标过程并获得正确的预测。这可以显着减少等待获取目标过程数据并快速实现良好过程模型的时间量。统计方法利用GPM转移知识。引入GPM以捕获从源过程传播到目标过程的不确定性。因此，目标模型的多步前预测可以提供平均预测，以及以预测方差的形式提供其预测的概率信息。最后，CSTR和Real Furnace系统用于展示所提出的方法的特征和对真实植物过程的适用性。

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