The use of artificial neural network for modeling in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables
The use of artificial neural network for modeling in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables
复制标题
使用 CNCPS 碳水化合物组分作为饮食变量，使用人工神经网络模拟体外瘤胃甲烷产生
DOI：
10.1016/j.livsci.2013.12.033
复制
发表时间：
2014-04-01
影响因子：
1.8
通讯作者：
Zhao, Guangyong
中科院分区：
文献类型：
--
作者：
Dong, Ruilan;Zhao, Guangyong
本试验的目的是探讨人工神经网络(ANN)对混合日粮中牛的瘤胃甲烷产量建模的适用性和准确性。采用三层BP神经网络进行建模，该网络包括输入层、隐含层和输出层。试验中使用的两个数据集来自董和赵(2013)。第一个数据集包含CH4、CO2和天然气总产量以及康奈尔碳水化合物和蛋白质系统(CNCPS)45份口粮的碳水化合物组分，用于训练BP模型，第二个数据集包含10份口粮，用于测试BP模型。比较了不同隐层神经元个数和不同输出层变量个数的BP模型的预测性能，建立了有效的BP模型。配对t检验表明，基于BP模型的CH4、CO2和总产气量的预测值与观测值之间没有差异(p>；0.05)。基于试验数据的模型性能分析表明，CH4、CO2和总气体的均方根预测误差(RMSPE%)分别为3.89%、2.95%和4.23%，实测值与预测值的决定系数(r(2))分别为0.95、0.97和0.92。对BP模型的检验表明，基于CNCPS碳水化合物组分的BP模型可以可靠、准确地预测牛混合日粮的体外CH4、CO2和总产气量。BP模型对甲烷产量的预测精度与多元回归模型相近，而对二氧化碳和天然气总产量的预测精度高于多元回归模型。(C)2014爱思唯尔B.V.保留所有权利。
The objective of this trial was to investigate the suitability and accuracy of modeling the rumen methane production of mixed rations for cattle using artificial neural network (ANN). The three layer back propagation neural network (BP) which included the input, the hidden and the output layers, was used for modeling. Two datasets used in the trial were from Dong and Zhao (2013). The first dataset which contained the CH4, CO2 and total gas production and the Cornell Net Carbohydrate and Protein System (CNCPS) carbohydrate fractions of forty-five rations was for training the BP model and the second dataset which contained ten rations was for testing the BP model. The predicting performances of the BP models with different number of neurons in the hidden layer and different number of variables in the output layer were compared, and the effective BP models were established. Paired t-test showed that no difference was found between the observed and the predicted CH4, CO2 and total gas production based on the BP models (p > 0.05). Model performance analysis based on the test data showed the root mean square prediction errors (RMSPE%) were 3.89%, 2.95% and 4.23%, and the determination coefficients (r(2)) between the observed and the predicted values were 0.95, 0.97 and 0.92 for CH4, CO2 and total gas, respectively. Testing of the BP models indicated that the in vitro CH4, CO2 and total gas production of mixed rations for cattle could be reliably and accurately predicted based on the CNCPS carbohydrate fractions using BP models. The BP models showed similar accuracy with the multiple regression model for predicting the CH4 production and better accuracy for predicting the CO2 and the total gas production than the multiple regression models. (C) 2014 Elsevier B.V. All rights reserved.