应用遗传算法和神经网络预测粮食总产量

摘要:通过对我国二十个省市粮食总产量的调查，分析影响粮食总产量的主要因素（农业机械总动力，有效灌溉面积，化肥施用量，农业从业人员）建立GA-BP神经网络模型，获得粮食总产量数学模型。同时，对此模型进行检测。在模型处理的过程中，应用遗传算法优化BP神经网络模型权值和阈值，获得优化后的网络模型，经过比较得出GA-BP神经网络模型在速度和精度上都高于BP神经网络的粮食总产量预测模型。

Application of Genetic Algorithm and Neural Network Forecasting of Food Production

Abstract: Through the total grain output of China's 20 provinces and cities in the survey, analyzing the main factors affecting food production (the total power of agricultural machinery, effective irrigated area, the amount of chemical fertilizer, agricultural employees) the establishment of GA-BP neural network model, access to food output mathematical model. At the same time, this model for testing. In the model the process of dealing with the application of genetic algorithm optimization model of BP neural network weights and thresholds to obtain the optimized network model, by comparison drawn GA-BP neural network model in terms of speed and accuracy is higher than the BP neural network grain yield prediction models