论文标题
Dynonet：用于学习动态系统的神经网络体系结构
dynoNet: a neural network architecture for learning dynamical systems
论文作者
论文摘要
本文介绍了一个名为Dynonet的网络体系结构，利用线性动力运算符作为基本构建块。由于这些块的动力学性质，Dynonet网络是为序列建模和系统识别目的而定制的。线性动力运算符相对于其参数及其输入序列的后传播行为。这使得对包含线性动力运算符和其他可区分单元的结构化网络进行端到端培训，从而利用现有的深度学习软件。示例显示了拟议方法对众所周知的系统识别基准的有效性。 示例显示了针对众所周知的系统识别基准的拟议方法的有效性。
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dynoNet networks are tailored for sequence modeling and system identification purposes. The back-propagation behavior of the linear dynamical operator with respect to both its parameters and its input sequence is defined. This enables end-to-end training of structured networks containing linear dynamical operators and other differentiable units, exploiting existing deep learning software. Examples show the effectiveness of the proposed approach on well-known system identification benchmarks. Examples show the effectiveness of the proposed approach against well-known system identification benchmarks.
