%0 Journal Article %A CHENG Yong %A WANG Jun %A ZHOU Xiangyu %T Agricultural greenhouse temperature prediction method based on improved deep belief network %D 2019 %R 10.11772/j.issn.1001-9081.2018091876 %J Journal of Computer Applications %P 1053-1058 %V 39 %N 4 %X 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. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018091876