标题： 基于数据驱动的Koopman算子预测与控制方法及模型平均使用 显示英文标题

标题： Data-driven Koopman Operator-based Prediction and Control Using Model Averaging

摘要： 这项工作提出了一种基于数据驱动的Koopman算子建模方法，该方法使用了模型平均技术。 尽管Koopman算子已被用于非线性动力系统的数据驱动建模和控制，但由于其无限维性和难以找到不变子空间，从数据中准确重建未知动力学并执行不同的决策任务仍然具有挑战性。 我们利用基于贝叶斯推断的模型平均技术的思想，设计了一种数据驱动的方法，该方法首先通过神经网络进行特征提取来生成多个Koopman模型，然后计算预测变量后验的点估计。 尽管集成中的每个模型可能不足以在广泛的工况点或未见过的数据上保持足够的准确性，但所提出的加权线性嵌入模型结合了模型集成的输出，旨在补偿每个模型的建模误差，从而提高整体性能。 显示英文摘要

摘要： This work presents a data-driven Koopman operator-based modeling method using a model averaging technique. While the Koopman operator has been used for data-driven modeling and control of nonlinear dynamics, it is challenging to accurately reconstruct unknown dynamics from data and perform different decision-making tasks, mainly due to its infinite dimensionality and difficulty of finding invariant subspaces. We utilize ideas from a Bayesian inference-based model averaging technique to devise a data-driven method that first populates multiple Koopman models starting with a feature extraction using neural networks and then computes point estimates of the posterior of predicted variables. Although each model in the ensemble is not likely to be accurate enough for a wide range of operating points or unseen data, the proposed weighted linear embedding model combines the outputs of model ensemble aiming at compensating the modeling error of each model so that the overall performance will be improved.