论文标题
非线性系统标识的深度状态空间模型
Deep State Space Models for Nonlinear System Identification
论文作者
论文摘要
Deep State Space模型（SSM）是一个积极研究的模型类，用于在深度学习社区中开发的时间模型，与经典SSM有着密切的联系。由于深神经网络的灵活性，将深SSM用作黑盒标识模型可以描述广泛的动态。此外，模型类的概率性质允许对系统的不确定性进行建模。在这项工作中，对深层SSM类及其参数学习算法进行了解释，以通过基于深度学习的方法扩展非线性识别方法的工具箱。在非线性系统识别基准的第一个统一实现中，评估了六个最新的SSM。
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. The use of deep SSMs as a black-box identification model can describe a wide range of dynamics due to the flexibility of deep neural networks. Additionally, the probabilistic nature of the model class allows the uncertainty of the system to be modelled. In this work a deep SSM class and its parameter learning algorithm are explained in an effort to extend the toolbox of nonlinear identification methods with a deep learning based method. Six recent deep SSMs are evaluated in a first unified implementation on nonlinear system identification benchmarks.
