Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining with Neural Networks for Wheat Yield Prediction
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
An Algorithm of Fertilization Model Fitting Based On Mixed Intelligent Computation
ICACC '09 Proceedings of the 2009 International Conference on Advanced Computer Control
| Hi-index | 0.98 |
There exists a nonlinear relationship between fertilizer input and soil nutrient level. To calculate the fertilization rate more precisely, a novel neural network ensemble method has been proposed, in which the K-means clustering method is used to select optimal networks individually and a Lagrange multiplier is used to combine these selected networks. On the basis of the above neural network ensemble method, a fertilization model is constructed. In this model, the soil nutrient level and the fertilization rate are taken as neural network inputs and the yield is taken as the output. This model transforms the calculation of the fertilization rate into solving a programming problem, and can be used to calculate the fertilization rate with maximum yield and maximum profit as well as to forecast the yield. Furthermore, this fertilization model has been tested on fertilizer effect data. The results show that the value forecast using the neural network ensemble is more accurate than that obtained with individual neural networks. The fertilization model constructed in this paper not only can precisely simulate the nonlinear relationship between yield and soil nutrient level, but also can adequately make use of the existing fertilizer effect data.