Abstract: The minimum and maximum temperature prediction model inside greenhouse planted Myica rubra was established based on BP neural network,by using meteorological data both inside and outside the greenhouse from December 2009 to June 2010 in Wenzhou of Zhejiang province. Using the independent experimental data and simulation back generations to verify the model,the results indicated that the root mean square error(RMSE) between the predicted value and measured value based on 1 line for the minimum and maximum and hourly inside air temperature were 0.8℃,1.4℃ and 0.7℃,respectively. The precision of BP neural network model was higher than that of the stepwise regression model obviously. The model,with few parameters,could predict the greenhouse temperature more accurately,which could provide scientific basis for facility meteorological service and environment regulation of greenhouse Myrica rubra cultivation.

Key words: font-family: 宋体, mso-bidi-font-family: 宋体, mso-font-kerning: 1.0pt, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">Neural network, Air temperature, Simulation model, Greenhouse planted font-family: 宋体, mso-ascii-font-family: MingLiU_HKSCS, mso-hansi-font-family: MingLiU_HKSCS, mso-bidi-font-family: MingLiU_HKSCS, mso-font-kerning: 1.0pt, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">font-family: 宋体, mso-bidi-font-family: 宋体, mso-font-kerning: 1.0pt, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">Myrica rubra