Knowledge Commons of Institute of Automation,CAS
Learning Greenhouse Climate Control Policy from Monitored Data | |
Xiaoxuan Zhao![]() ![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | 2022 China Automation Congress (CAC) |
页码 | 6731-6736 |
会议日期 | Nov. 25 - 27, 2022 |
会议地点 | 厦门 · China |
产权排序 | 2 |
摘要 | The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verifified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar confifiguration |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 人工智能+农业 |
国重实验室规划方向分类 | 实体人工智能系统决策-控制 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51585 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Xiaoxuan Zhao,Haoyu Wang,Xiujuan Wang,et al. Learning Greenhouse Climate Control Policy from Monitored Data[C],2022:6731-6736. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Learning_Greenhouse_(2021KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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