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Position: Home > Articles > 基于EMD-LSTM的猪舍氨气浓度预测研究 Transactions of the Chinese Society for Agricultural Machinery 2019 (1) 353-360

基于EMD-LSTM的猪舍氨气浓度预测研究

作  者：
杨亮;刘春红;郭昱辰;邓河;李道亮;段青玲
单  位：
中国农业大学信息与电气工程学院
关键词：
猪舍;氨气浓度;经验模态分解;长短时记忆神经网络
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/m3、0. 6257%和0. 094 5 mg/m3.
作  者：
YANG Liang;LIU Chunhong;GUO Yuchen;DENG He;LI Daoliang;DUAN Qingling;College of Information and Electrical Engineering,China Agricultural University;Beijing Engineering and Technology Research Center for Internet of Things in Agriculture;
关键词：
piggeries;;ammonia concentration;;empirical mode decomposition;;long short-term memory neural network
摘  要：
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~3,respectively.

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