Short-Term Predicting Model for Dissolved Oxygen of Hyriopsis Cumingii Ponds Based on Elman Neural Network

摘要：

In the management of Hyriopsis Cumingii ponds, dissolved oxygen (DO) is the key point to measure, predict and control. This paper analyzes the important factors for predicting dissolved oxygen of Hyriopsis Cumingii ponds in short-term period, and finally chooses solar radiation (SR), air temperature (AT), water temperature (WT), wind speed (WS), PH and oxygen (DO) as six input parameters. As the dissolved oxygen in the outdoor pond is low controllability and scalability, this paper pro-poses a short-term predicting model for dissolved oxygen based on Elman neural network. Then the predicting model is trained, tested and compared with BP model. Experimental results show that: Elman neural network predicting model with good fitting ability, generalization ability, and high prediction accuracy, can better predict the short-term changes of dissolved oxygen than BP neural network. This Elman neural network model is proven to be an effective way to predict dissolved oxygen in short-term.

关键词： Dissolved Oxygen Elman Neural Network Short-Time Prediction Hyriopsis Cumingii

作者: Mingxia Yan Daoliang Li Yaoguang Wei Haijiang Tai Qisheng Ding

作者单位: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P. R. China

会议类型: 国际会议

会议名称: The 4th IFIP International on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information(第四届国际计算机及计算机技术在农业中的应用研讨会暨第四届中国农业信息化发展论坛 CCTA 2010)

会议地点: 南昌

会议语种:英文

页码: 1014-1019

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）