基于改进深度信念网络的农业温室温度预测方法

doi:10.11772/j.issn.1001-9081.2018091876

计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 1053-1058.DOI: 10.11772/j.issn.1001-9081.2018091876

基于改进深度信念网络的农业温室温度预测方法

周翔宇¹, 程勇², 王军^1,2

1. 南京信息工程大学计算机与软件学院, 南京 210044;
2. 南京信息工程大学科技产业处, 南京 210044

收稿日期:2018-09-10 修回日期:2018-11-20 出版日期:2019-04-10 发布日期:2019-04-10
通讯作者: 周翔宇
作者简介:周翔宇(1993-),男,江苏淮安人,硕士研究生,主要研究方向:大数据、深度学习;程勇(1980-),男,重庆人,高级工程师,博士,CCF会员,主要研究方向:无线传感器网络、大数据;王军(1970-),男,安徽铜陵人,教授,博士,CCF会员,主要研究方向:无线传感器网络、大数据。
基金资助:
国家自然科学基金资助项目（41875184）；江苏省"六大人才高峰"项目（2015-DZXX-015，TD-XYDXX-004）；江苏高校"青蓝工程"项目。

Agricultural greenhouse temperature prediction method based on improved deep belief network

ZHOU Xiangyu¹, CHENG Yong², WANG Jun^1,2

1. School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
2. Division of Science & Technology, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2018-09-10 Revised:2018-11-20 Online:2019-04-10 Published:2019-04-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (41875184), the Six Talent Peaks Project in Jiangsu Province (2015-DZXX-015, TD-XYDXX-004), the Qing Lan Project of Jiangsu Province.

摘要/Abstract

摘要： 针对浅层神经网络面对温室复杂多变环境因子表征能力低、学习时间长的问题，提出一种基于改进深度信念网络并结合经验模态分解与门控循环单元的温室预测方法。首先，通过经验模态分解将温度环境因子进行信号分解，之后将分解出来的固有模态函数与残差信号进行不同程度的预测；然后，引入神经胶质改进深度信念网络，并将分解信号结合光照和二氧化碳进行多属性的特征提取；最后，将门控循环单元预测的信号分量相加获得最终的预测结果。仿真实验结果表明，与经验模态分解-深度信念网络（EMD-DBN）和深度信念网络-神经胶质链（DBN-g）相比，所提方法的预测误差分别降低了6.25%和5.36%，验证了其在强噪声、强耦合的温室时序环境下预测的有效性和可行性。

关键词: 循环神经网络, 深度信念网络, 门控循环单元, 时间序列预测, 神经胶质链

Abstract: Concerning low representation ability and long learning time for complex and variable environmental factors in greenhouses, a prediction method based on improved Deep Belief Network (DBN) combined with Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) was proposed. Firstly, the temperature environment factor was decomposed by EMD, and then the decomposed intrinsic mode function and residual signal were predicted at different degrees. Secondly, glia was introduced to improve DBN, and the decomposition signal was used to multi-attribute feature extraction combined with illumination and carbon dioxide. Finally, the signal components predicted by GRU were added together to obtain the final prediction result. The simulation results show that compared with empirical decomposition belief network (EMD-DBN) and glial DBN-glial chains (DBN-g), the prediction error of the proposed method is reduced by 6.25% and 5.36% respectively, thus verifying its effectiveness and feasibility of predictions in greenhouse time series environment with strong noise and coupling.

Key words: recurrent neural network, Deep Belief Network (DBN), Gated Recurrent Unit (GRU), time series prediction, glial chain

中图分类号:

TP183

周翔宇, 程勇, 王军. 基于改进深度信念网络的农业温室温度预测方法[J]. 计算机应用, 2019, 39(4): 1053-1058.

ZHOU Xiangyu, CHENG Yong, WANG Jun. Agricultural greenhouse temperature prediction method based on improved deep belief network[J]. Journal of Computer Applications, 2019, 39(4): 1053-1058.

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基于改进深度信念网络的农业温室温度预测方法

Agricultural greenhouse temperature prediction method based on improved deep belief network

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