基于PCA-LM-NARX的禽舍室温预测模型
TP274+2%S244; 采用隧道式通风系统的禽舍室内温度容易受自然环境变化以及家禽日龄影响,难以在线准确预测.为了准确预测禽舍室内温度,该研究结合主成分分析法(principal component analysis,PCA)、莱温伯格-马夸特算法(Levenberg-Marquardt method,LM)和带外部输入的非线性自回归模型(nonlinear auto-regressive model with exogenous inputs,NARX),提出了一种PCA-LM-NARX的方法用于在线构建禽舍室内温度预测模型.该方法利用主成分分析提取影响禽舍室内温度的关键环境变量,构建基...
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| Published in | 农业工程学报 Vol. 41; no. 2; pp. 261 - 270 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
山东科技大学电气与自动化工程学院,青岛 266590%青岛农业大学机电工程学院,青岛 266109
2025
东营青农大盐碱地高效农业技术产业研究院,东营 257029 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202404225 |
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| Abstract | TP274+2%S244; 采用隧道式通风系统的禽舍室内温度容易受自然环境变化以及家禽日龄影响,难以在线准确预测.为了准确预测禽舍室内温度,该研究结合主成分分析法(principal component analysis,PCA)、莱温伯格-马夸特算法(Levenberg-Marquardt method,LM)和带外部输入的非线性自回归模型(nonlinear auto-regressive model with exogenous inputs,NARX),提出了一种PCA-LM-NARX的方法用于在线构建禽舍室内温度预测模型.该方法利用主成分分析提取影响禽舍室内温度的关键环境变量,构建基于关键环境变量的NARX神经网络室温预测模型,并利用LM算法对神经网络参数进行优化计算.考虑到禽舍室温变化的滞后特性,PCA-LM-NARX方法利用贝叶斯信息准则设计NARX神经网络的最优延迟阶数.建模过程中PCA-LM-NARX方法采用移动窗法在线更新室温预测模型参数,以适应不同日龄的家禽和自然环境的变化.试验结果显示,基于PCA-LM-NARX方法构建的室温预测模型预测未来 5、15、30 min温度值的均方误差大小分别为 0.0220、0.0472、0.0779℃2;在i5-12500H型CPU上运行建模程序,平均建模用时为 0.3321 s.研究结果表明,PCA-LM-NARX方法可以构建高精度禽舍室温预测模型,并实现模型参数的快速在线更新. |
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| AbstractList | TP274+2%S244; 采用隧道式通风系统的禽舍室内温度容易受自然环境变化以及家禽日龄影响,难以在线准确预测.为了准确预测禽舍室内温度,该研究结合主成分分析法(principal component analysis,PCA)、莱温伯格-马夸特算法(Levenberg-Marquardt method,LM)和带外部输入的非线性自回归模型(nonlinear auto-regressive model with exogenous inputs,NARX),提出了一种PCA-LM-NARX的方法用于在线构建禽舍室内温度预测模型.该方法利用主成分分析提取影响禽舍室内温度的关键环境变量,构建基于关键环境变量的NARX神经网络室温预测模型,并利用LM算法对神经网络参数进行优化计算.考虑到禽舍室温变化的滞后特性,PCA-LM-NARX方法利用贝叶斯信息准则设计NARX神经网络的最优延迟阶数.建模过程中PCA-LM-NARX方法采用移动窗法在线更新室温预测模型参数,以适应不同日龄的家禽和自然环境的变化.试验结果显示,基于PCA-LM-NARX方法构建的室温预测模型预测未来 5、15、30 min温度值的均方误差大小分别为 0.0220、0.0472、0.0779℃2;在i5-12500H型CPU上运行建模程序,平均建模用时为 0.3321 s.研究结果表明,PCA-LM-NARX方法可以构建高精度禽舍室温预测模型,并实现模型参数的快速在线更新. |
| Abstract_FL | Tunnel-ventilated poultry houses have been widely used for the breeding industry in recent years.However,it is difficult to accurately predict online the room temperature of tunnel-ventilated poultry houses,due to the variations in the natural environment and the age of the poultry.This study aims to accurately predict the room temperature of poultry houses for the high accuracy of temperature regulation.A PCA-LM-NARX method was also proposed to construct the online prediction model for the room temperature of poultry houses.The resulting NARX neural network model was used to accurately predict the short-term indoor temperature,according to the recent environmental data of poultry houses.The environmental variables were selected using the PCA-LM-NARX method,according to the energy balance equation of the temperature system in the poultry house.Principal component analysis(PCA)was also utilized to screen out the key influencing factors on the indoor temperature of the poultry house from multiple environmental variables.A prediction model of room temperature was constructed with a NARX neural network structure.The LM algorithm was also used to optimize the model parameters.The hysteresis characteristics of indoor temperature were considered in poultry houses,due mainly to the heat conduction delay,air circulation delay,control equipment adjustment delay,and evaporative cooling.The optimal delay order of the NARX neural network was designed using the Bayesian information criterion.The normalization and rolling statistical methods were used to preprocess the measured data.The PCA-LM-NARX method was modified suitable for poultry of different ages and environmental changes during modeling.Furthermore,the moving window was used to remove the previous data with a large time span from the current moment in the neural network training set,and then the new data was added with a short time span from the current moment.The real-time updates were achieved on the training set,thereby reducing the negative impact of previous data on the prediction performance of the model.The modeling program was divided into two parts:offline modeling and online prediction.The offline modeling was installed on the host computer,in order to update the parameters of the prediction model in real-time using the data in the moving window.Once the LM algorithm met the requirements of accuracy,the optimal parameters were transmitted to the field controller through the fieldbus network.The online prediction program and room temperature prediction model were stored in the on-site controller for the online prediction of poultry house temperature.The field controller was used to transmit the optimal training dataset to the host computer through the fieldbus network,in order to update the parameters of the prediction model.Experimental results show that the prediction model with the PCA-LM-NARX method can be expected to predict the room temperature of the poultry house in the next 5,15,and 30 min online,with the mean squared error(MSE)of 0.0220,0.0472 and 0.0779℃2,respectively.The average modeling duration of the running program on the i5-12500H CPU was 0.3321s,indicating the rapid real-time update of model parameters.Therefore,the PCA-LM-NARX method can be used to construct a high-precision prediction model for the room temperature of a tunnel-ventilated poultry house,in order to achieve the rapid online update of model parameters. |
| Author | 高鲁宁 贺凯迅 钟宁帆 李娟 |
| AuthorAffiliation | 山东科技大学电气与自动化工程学院,青岛 266590%青岛农业大学机电工程学院,青岛 266109;东营青农大盐碱地高效农业技术产业研究院,东营 257029 |
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| Author_FL | HE Kaixun LI Juan ZHONG Ningfan GAO Luning |
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| Author_xml | – sequence: 1 fullname: 钟宁帆 – sequence: 2 fullname: 高鲁宁 – sequence: 3 fullname: 贺凯迅 – sequence: 4 fullname: 李娟 |
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| ClassificationCodes | TP274+2%S244 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Poultry house room temperature prediction model based on PCA-LM-NARX |
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| Keywords | 温度 主成分分析法 prediction model 隧道式通风系统 principal component analysis method NARX神经网络 temperature 预测模型 NARX neural network poultry house tunnel ventilation system 禽舍 |
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| Snippet | TP274+2%S244; 采用隧道式通风系统的禽舍室内温度容易受自然环境变化以及家禽日龄影响,难以在线准确预测.为了准确预测禽舍室内温度,该研究结合主成分分析法(principal component analysis,PCA)、莱温伯格-马夸特算法(Levenberg-Marquardt... |
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| Title | 基于PCA-LM-NARX的禽舍室温预测模型 |
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