LM-BP神经网络在农业总产值预测的应用

Application of LM-BP Neural Network in Predicting Gross Agricultural Product

农业生产总值是衡量一个地区农业发展水平的重要指标,农业生产总值受多方因素的影响,具有非线性的特征,为此,提出了LM-BP神经网络预测农业生产总值的模型及方法.以农作物播种面积、粮食产量、甘蔗产量、木薯产量、茶叶产量、肉类产量、水产品产量、松脂产量及油茶籽产量等与农业生产总值相关指标作为网络输入,通过广西2000 ～2012年农业生产总值数据仿真试验分析表明,LM-BP神经网络预测结果与实际值有较好的拟合度.

Gross agricultural product is an important indication to measure the agricultural development level of a region.It would be affected by many factors,owning the character of non-linearity.For this reason,LM-BP neural network was put forward as the model and method for predicting gross agricultural product.Taking the indications of the sown area of crop,the output of grain,sugarcane,cassava,tea,meat,aquatic products,turpentine and oil-tea camellia seed,etc.as inputs,during 2000 to 2012 in Guangxi,the gross agricultural product data from the analysis of simulation experiment shows that the prediction of LM-BP neural network fits well with actual results.