深度学习
人工智能
计算机科学
美国宇航局深空网络
非线性系统
鉴定(生物学)
黑匣子
系统标识
工具箱
状态空间
人工神经网络
班级(哲学)
机器学习
灵活性(工程)
非线性系统辨识
算法
数据挖掘
数学
工程类
统计
生物
物理
航空航天工程
量子力学
植物
程序设计语言
航天器
度量(数据仓库)
作者
Daniel Gedon,Niklas Wahlström,Thomas B. Schön,Lennart Ljung
标识
DOI:10.1016/j.ifacol.2021.08.406
摘要
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.
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