深度学习在控制领域的研究现状与展望
段艳杰1; 吕宜生1; 张杰1,2; 赵学亮1; 王飞跃1
Source Publication自动化学报
2016
Volume42Issue:5Pages:643-654
Abstract深度学习在特征提取与模型拟合方面显示了其潜力和优势. 对于存在高维数据的控制系统, 引入深度学习具有一定
的意义. 近年来, 已有一些研究关注深度学习在控制领域的应用. 本文介绍了深度学习在控制领域的研究方向和现状, 包括控
制目标识别、状态特征提取、系统参数辨识和控制策略计算. 并对相关的深度控制以及自适应动态规划与平行控制的方法和思
想进行了描述. 总结了深度学习在控制领域研究中的主要作用和存在的问题, 展望了未来值得研究的方向.
Other AbstractDeep learning has shown great potential and advantage in feature extraction and model fitting. It is significantto use deep learning for control problems involving high dimension data. Currently, there have been some investigations focusing on deep learning in control. This paper is a review of related work including control object recognition, state feature extraction, system parameter identification and control strategy calculation. Besides, this paper describes the approaches and ideas of deep control, adaptive dynamic programming and parallel control related to deep learning in control. Also, this paper summarizes the main functions and existing problems of deep learning in control, presents some prospects of future work.
Keyword深度学习 控制 特征 自适应动态规划
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11976
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding Author王飞跃
Affiliation1.中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京,100190
2.青岛智能产业技术研究院,青岛,266109
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
段艳杰,吕宜生,张杰,等. 深度学习在控制领域的研究现状与展望[J]. 自动化学报,2016,42(5):643-654.
APA 段艳杰,吕宜生,张杰,赵学亮,&王飞跃.(2016).深度学习在控制领域的研究现状与展望.自动化学报,42(5),643-654.
MLA 段艳杰,et al."深度学习在控制领域的研究现状与展望".自动化学报 42.5(2016):643-654.
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