农田水位及环境因素对小麦生理指标的影响预测

通过对人工神经网络理论中BP网络的分析,建立了描述在不同水位、不同环境条件下,小麦生理指标非线形变化的模拟模型。该模型采用光合有效辐射、太阳总辐射、大气温度、空气相对湿度、风速、农田水位、淹水历时等为参数,以2009年在测坑的小麦试验结果作为学习样本和检验样本。结果表明,所建立的人工神经网络模型对描述不同条件与净光合速率、蒸腾速率、气孔导度的复杂非线性关系方面具有较高的精度和应用价值。

Prediction of the Influence of Water Level and Environmental Factors on Physiological Index of Wheat

Through the analysis of the BP network in artificial neural network theory,a simulation model was built to describe the non-linear variation of the physiological indices of wheat at different water level and under different environmental conditions.The model took the photosynthetic active radiation,global solar radiation,air temperature,air relative humidity,wind speed,farmland water levels and flooding lasting as the parameters,and chose the wheat pit test results in 2009 as the study and test samples.The ...