基于BP神经网络的农业原材料价格预测

文章基于几种常见的农业原材料(粗羊毛、细羊毛、椰干和棉花)价格数据利用BP神经网络算法对它们的价格进行了预测。研究结果表明:BP神经网络对农业原材料的价格有较强的预测能力,预测值与真实值十分接近,预测准确率都能达到90%以上;BP神经网络对棉花价格的预测准确率最高,粗羊毛和细羊毛次之,对椰干价格的预测准确率最低,这与椰干价格的不稳定性有关。

Based on the price data of several common agricultural raw materials(coarse wool,fine wool,copra and cotton),this paper uses the BP neural network algorithm to predict their prices.The results of the study indicate:The BP neural network has a strong ability to predict the price of agricultural raw materials,and the predicted value is very close to the real value,and the prediction accuracy can reach more than 90%;the BP neural network has the highest prediction accuracy of the cotton price,coarse wool and fine wool Secondly,the prediction accuracy of copra prices is the lowest,which is related to the instability of copra prices.