一种新的神经网络集成方法及其在精准施肥中的应用

为解决作物精准施肥量确定这一难题，提出了一种新的基于神经网络集成的精准施肥量确定方法.在该方法中，采用回放取样生成神经网络个体集合，通过给出一种神经网络相似度度量标准，用聚类算法AP从神经网络个体集合中选出一组精度高、多样性强的网络个体；进而形成分别用拉格朗日乘子和预测有效度法线性集成所选个体的算法LME和FEME.在基准数据集上的实验结果表明：在精度方面，算法LME要明显优于算法FEME和算法BSN(单个最优神经网络算法)，且LME具有较好的泛化能力.最后在确定精准施肥量方面，对算法LME进行了实际应用，结果表明LME明显优于传统施肥模型和现有神经网络精准施肥模型.

A Novel Neural Network Ensemble Method and Its Application in Precision Fertilization

In order to solve the problem of precise fertilization rate determination, a novel neural network ensemble method is introduced in this paper. In this method, the method of sampling with replacement is used to produce neural network individual set. A novel formula measuring the network similarity is given and Freys clustering algorithm AP is used to select the networks with high precision and greater diversity. Then by the Lagrange multiplier ensemble(LME) and forecasting effective measure mensemble(FEME) method, these selected networks are combined. The experiment on the standard dataset shows that, LME algorithm has higher accuracy and stronger generalization than the single neural network. Furthermore, as a linear weighted ensemble method, LME is better than FEME. Generally, the root mean squared error (RMSE) decreases when the subnets number and cluster number increases, but when the number reaches a level, the decreasing trend of RMSE becomes slow. Then according to the fertilizer effect data on maize field in black soil, LM ensemble is used to build the precision fertilization model, where soil nutrient and target yield are taken as inputs and fertilization rate is taken as output. The practice shows that LM ensemble based fertilization model is better than traditional fertilization models and the existing neural network based fertilization models.