用于控制系统的时间一致性双线性循环自编码器
Temporally-Consistent Bilinearly Recurrent Autoencoders for Control Systems
AI总结：
针对传统Koopman自编码器在有限含噪数据下泛化不足的问题，提出基于Koopman算子的tcBLRAN架构，通过一致双线性循环动力学实现控制仿射系统长期准确预测，性能优于现有最优方法。
摘要：
本文提出了时间一致性双线性循环自编码器（tcBLRAN），这是一种基于Koopman算子的神经网络架构，用于建模控制仿射非线性控制系统。该方法通过引入在预测过程中保持一致的双线性循环动力学，对传统Koopman自编码器（KAE）进行扩展，能够实现对控制仿射系统的准确长期预测。这一改进解决了KAE在面对有限且含噪的训练数据集时，因训练数据不一致导致泛化能力不足的障碍。通过融合深度学习与动力系统理论，tcBLRAN在捕捉复杂行为和控制系统动力学方面表现出优异性能，为控制系统提供了一种更优的数据驱动建模技术，性能优于由自编码器网络学习的当前最优Koopman双线性形式（KBF）。
英文摘要：
This paper introduces the temporally-consistent bilinearly recurrent autoencoder (tcBLRAN), a Koopman operator based neural network architecture for modeling a control-affine nonlinear control system. The proposed method extends traditional Koopman autoencoders (KAE) by incorporating bilinear recurrent dynamics that are consistent across predictions, enabling accurate long-term forecasting for control-affine systems. This overcomes the roadblock that KAEs face when encountered with limited and noisy training datasets, resulting in a lack of generalizability due to inconsistency in training data. Through a blend of deep learning and dynamical systems theory, tcBLRAN demonstrates superior performance in capturing complex behaviors and control systems dynamics, providing a superior data-driven modeling technique for control systems and outperforming the state-of-the-art Koopman bilinear form (KBF) learned by autoencoder networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
