基于BP神经网络的光照分布研究

光照度对植物的生长发育有着不可忽视的重要作用，它的强弱直接影响到植物的光合作用、物质代谢、结构形态和农作物产量等。通过重点研究大规模样本点的BP神经网络空间插值方法，进而提高计算光照分布的准确性和有效性。利用分组训练、组合优化的改进BP神经网络的权值和阈值，训练BP神经网络预测模型求得最优解。实验结果表明该模型的相对误差较小，对光照度预测的可信度较高，可以用于预测光照分布。

Research on the Distribution of Light Based on BP Neural Network

Light inte nsity on plant growth and development has an important role that directly affects the strength of plant photosynthesis, metab-olism, morphology and crop yields. Focuses on the large-scale sample points through BP neural network spatial interpolation methods, thus improving the accuracy of calculation of light distribution and effectiveness. The use of packet training, combinatorial optimization of improved BP neural network weights and thresholds, the training BP neural network prediction model optimal solutions. Experimental re-sults show that the model is small relative error on the credibility of the higher light intensity projections can be used to predict the light distribution.