Abstract： To solve the problem of feedforward neural network for predicting grain yield with easy falling into local optimum, an improved BP neural network model was proposed based on particle swarm optimization and artificial bee colony algorithm. According to the different advantages of particle swarm optimization algorithm and artificial bee colony algorithm in global search ability, the weight and the threshold of BP neural network were further optimized to improve the accuracy and robustness of grain yield prediction model. The specific implementation of artificial bee particle swarm optimization(ABPSO) algorithm was given based on particle swarm and artificial bee colony. The eight factors on Chinese grain yield and the yields from 1979 to 2012 were selected as data sets. The results show that the trend of grain yield in China in recent years can be better predicted by the improved BP neural network. Compared with unimproved BP model, the average prediction error of the new algorithm is decreased from 847 780 t to 240 320 t, and the error range is decreased from 1 894 200 t to 586 800 t.
2019, Vol. 40 
