| 基于LSTM-Kalman模型的蛋鸡产蛋率预测方法 |
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| 引用本文： | 吉训生,姜晓卫,夏圣奎.基于LSTM-Kalman模型的蛋鸡产蛋率预测方法[J].浙江农业学报,2021,33(9):1730. |
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| 作者姓名： | 吉训生 姜晓卫 夏圣奎 |
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| 作者单位： | 1.江南大学 物联网工程学院,江苏 无锡 2141222.南通天成现代农业科技有限公司,江苏 南通 226600 |
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| 基金项目： | 江苏省重点研发计划(现代农业)(BE2018334) |
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| 摘 要： | 产蛋率是评价蛋鸡产蛋性能的重要指标之一,因其具有时序性和非线性等特点,且其影响变量众多、存在复杂的耦合关系,难以实现精准预测。由于传统神经网络预测过程的非记忆性难以处理时序性问题,该文章提出蛋鸡产蛋率的LSTM-Kalman预测方法,使用主成分分析提取影响蛋鸡产蛋率的关键变量,通过LSTM神经网络预测蛋鸡产蛋率,采用Kalman滤波对LSTM预测的结果进行动态调整,作为最终预测结果。数据分析结果表明:LSTM-Kalman模型预测产蛋率的平均绝对误差、均方误差和皮尔逊相关系数分别为0.312 8、0.435 3和0.975 2,明显优于传统的BP神经网络、极限学习机等预测方法;通过2栋鸡舍生产数据的交叉测试验证,模型的预测准确率达到97.14%和98.71%,表明模型具有较强的泛化能力,能够满足蛋鸡产蛋率预测的实际需要,可以为蛋鸡养殖环境数据的精准调控提供参考。
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| 关 键 词： | 蛋鸡产蛋率 Kalman滤波 LSTM神经网络 预测准确率 |
| 收稿时间： | 2020-11-19 |
Research on prediction of laying rate by hens based on LSTM-Kalman model |
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| JI Xunsheng,JIANG Xiaowei,XIA Shengkui.Research on prediction of laying rate by hens based on LSTM-Kalman model[J].Acta Agriculturae Zhejiangensis,2021,33(9):1730. |
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| Authors: | JI Xunsheng JIANG Xiaowei XIA Shengkui |
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| Institution: | 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
2. Nantong Tiancheng Modern Agricultural Technology Co., Ltd., Nantong 226600, China |
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| Abstract: | Laying rate is one of the important indexes to evaluate laying performance of hens, it has the characteristics of time-varying, nonlinearity, and complex coupling. So it is difficult to predict the laying rate accurately. The traditional neural network prediction model does not have memory function and cannot be used on the time sequence prediction. So the LSTM-Kalman prediction model is proposed. Firstly, principal component analysis was used to extract the key influencing variables of laying rate of hens. Then LSTM neural network was used as a static prediction model to predict the laying rate of hens. Kalman filter was used to dynamically adjust the result of LSTM prediction to obtain the final prediction results. The data analysis showed that the model’s average absolute error, mean square error and Pearson correlation coefficient were 0.312 8, 0.435 3 and 0.975 2, respectively, which was significantly better than traditional prediction methods, including BP neural network and extreme learning machine. The mutual testing and verification, based on the production data of two hen coops, showed the prediction accuracy of the model were 97.14% and 98.71%, respectively. The model had strong generalization ability and could meet the actual needs of layer production rate prediction. This paper provided a reference for precise control of layer breeding environment data. |
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| Keywords: | laying rate of hens Kalman filter LSTM neural network prediction accuracy |
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