基于径向基神经网络农机数量预测的研究  

Research on forecast of the number of agricultural machinery based on RBF neural network

作　　者：尤文坚[1] 叶雪英[2] 唐仕云 

机构地区：[1]广西农业职业技术学院电子信息工程系,南宁市530007 [2]广西壮族自治区亚热带作物研究所,南宁市530001 [3]亚热带农业生物资源保护与利用国家重点实验室,南宁市530005

出　　处：《中国农机化学报》2013年第2期38-41,共4页Journal of Chinese Agricultural Mechanization

基　　金：广西教育厅自然科学科研项目资助(项目编号:200911LX547)

摘　　要：提出了利用径向基神经网络对农机数量预测的方法 ,通过选取合适的训练参数,径向基网络能够得到满足要求的预测结果,农机总动力、拖拉机数量、农具数量的误差平方和分别为0.0056、0.0470、0.2713。利用测试集对网络进行测试,农机总动力预测值与真实值的误差最大为-7.17%,最小为0.22209%。研究结果表明,径向基神经网络能有效提高预测精确度,较好地预测非线性条件下的农机数量,证明了实验方法的有效性和可行性,为人工神经网络在农业机械化的应用提供了一个新的途径。Put forward a method to predict the number of agricultural machinery by using RBF NN.By choosing proper training parameters,RBF network can get the predicted results of meeting the requirements.The error sum of squares of agricultural machinery total power,tractor number and farm implements number is 0.0056,0.0470,0.271,to test the network Using test sets.The maximum error of agricultural machinery total power between predicted value and real value is-7.17%.The minimum is 0.22209%.The research results indicate that RBF NN can effectively increase the prediction accuracy.The number of agricultural machinery under the condition of nonlinear can be well predicting.Experimental method is proved to be feasible and effective.It provides a new way for the application of artificial neural network in agricultural mechanization.

关 键 词：径向基 神经网络 农机 预测 

分 类 号：S24[农业科学—农业电气化与自动化]