【摘要】 综合考虑影响粮食产量的多种因素,运用改进的粒子群算法优化BP神经网络的初始权重,建立了适合小样本粮食产量的预测模型.实验表明,与BP神经网络粮食预测模型和PSO-BP神经网络粮食预测模型相比,该模型具有更高的预测精度和较大的适应度.更多还原

【Abstract】 This study considers comprehensively the various factors of grain production yield and optimizes primary BP neural network weights using the improved Particle Swarm Optimization(PSO) algorithm, then establishes a prediction model suitable for prediction of small sample grain yield. The experiment proves that this model has higher prediction precision and greater fitness than grain yield prediction model based on classical BP neural network and PSO-BP neural network.更多还原