杨亮,刘春红,郭昱辰,邓河,李道亮,段青玲.基于EMD-LSTM的猪舍氨气浓度预测研究[J].农业机械学报,2019,50(Supp):353-360.
YANG Liang,LIU Chunhong,GUO Yuchen,DENG He,LI Daoliang,DUAN Qingling.Prediction of Ammonia Concentration in Fattening Piggery Based on EMD-LSTM[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(Supp):353-360.
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基于EMD-LSTM的猪舍氨气浓度预测研究   [下载全文]
Prediction of Ammonia Concentration in Fattening Piggery Based on EMD-LSTM   [Download Pdf][in English]
投稿时间:2019-04-25  
DOI:10.6041/j.issn.1000-1298.2019.S0.054
中文关键词:  猪舍  氨气浓度  经验模态分解  长短时记忆神经网络
基金项目:国家重点研发计划项目(2016YFD0700200)
作者单位
杨亮 中国农业大学 
刘春红 中国农业大学
北京市农业物联网工程技术研究中心 
郭昱辰 中国农业大学 
邓河 中国农业大学 
李道亮 中国农业大学
北京市农业物联网工程技术研究中心 
段青玲 中国农业大学
北京市农业物联网工程技术研究中心 
中文摘要:为提高猪舍氨气浓度预测的精度和效率,提出了基于经验模态分解和长短时记忆神经网络(EMD-LSTM)的猪舍氨气浓度预测模型。首先,将猪舍氨气浓度时间序列数据进行经验模态分解,得到不同时间尺度下的固有模态分量(IMF);然后,对IMF建立LSTM氨气浓度预测模型;最后,将各分量的预测结果相加求和作为猪舍氨气浓度的最终预测值。将本文提出的预测模型应用于江苏省宜兴市实验基地某养猪场的氨气浓度预测中,并与Elman模型、循环神经网络(RNN)模型、LSTM模型和EMD-LSTM模型进行了对比实验,结果表明,基于EMD-LSTM模型的预测精度较高,预测结果与真实值相比较,平均绝对误差、平均绝对百分误差和均方根误差为0.0723mg/m3、0.6257%和0.0945mg/m3。
YANG Liang  LIU Chunhong  GUO Yuchen  DENG He  LI Daoliang  DUAN Qingling
China Agricultural University,China Agricultural University;Beijing Engineering and Technology Research Center for Internet of Things in Agriculture,China Agricultural University,China Agricultural University,China Agricultural University;Beijing Engineering and Technology Research Center for Internet of Things in Agriculture and China Agricultural University;Beijing Engineering and Technology Research Center for Internet of Things in Agriculture
Key Words:piggeries  ammonia concentration  empirical mode decomposition  long short term memory neural network
Abstract:Ammonia is one of the key environmental parameters affecting the healthy growth of pigs. And it is the key to ensure the healthy growth of pigs by timely and accurately grasping the trend of ammonia concentration in piggeries. In order to improve the accuracy and efficiency of ammonia concentration prediction in piggeries, a prediction model of ammonia concentration in piggeries based on empirical mode decomposition and long short term memory neural network (EMD-LSTM) was proposed. Firstly, the sequence data of ammonia concentration was decomposed to obtain the intrinsic mode function (IMF) at different time scales. Then, the long term memory neural network prediction model was established for the intrinsic mode function. Finally, the prediction results of the components were summed as the final value of the concentration. The prediction model proposed was applied to the prediction of ammonia concentration in a pig farm in Yixing, Jiangsu Province. In order to verify the performance of the prediction model, the prediction model was compared with Elman prediction model, recurrent neural network (RNN) prediction model, long term memory neural network prediction model and empirical mode decomposition and recurrent neural network prediction model. The results showed that the prediction accuracy of the empirical mode decomposition and long term memory neural network model was higher. Compared with the real values, the mean absolute error, mean absolute percentage error and root mean square error were 0.0723mg/m3,0.6257% and 0.0945mg/m, respectively.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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