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A Grain Output Combination Forecast Model Modified by Data Fusion Algorithm

Abstract To deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of – particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm – to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.