将深度学习融入系统辨识工具，实现复杂动态系统的高效建模。
Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
- 用神经状态空间模型结合自编码器，实现大系统降维建模
- 支持原始数值矩阵与时间表直接训练，提升数据处理效率
- 集成自动微分技术，优化状态估计性能，适合工业控制应用
过去三年中，MATLAB® 的系统辨识工具箱持续增强动态建模能力，重点在于整合深度学习架构与训练方法，使深度神经网络可作为非线性模型的构建模块。该工具箱提供神经状态空间模型，并可通过自编码功能实现大型系统的降维建模。此外，工具箱还引入多项改进：深化与前沿机器学习技术的集成，利用自动微分特性进行状态估计，支持直接使用原始数值矩阵和时间表进行模型训练，显著提升建模效率与工程适用性。
原文摘要 · Abstract (English)
MATLAB(R) releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the System Identification Toolbox(TM). The emphasis has been on integrating deep learning architectures and training techniques that facilitate the use of deep neural networks as building blocks of nonlinear models. The toolbox offers neural state-space models which can be extended with auto-encoding features that are particularly suited for reduced-order modeling of large systems. The toolbox contains several other enhancements that deepen its integration with the state-of-art machine learning techniques, leverage auto-differentiation features for state estimation, and enable a direct use of raw numeric matrices and timetables for training models.
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