Neural network based method for compensating model error  

基于神经网络方法的模型误差补偿(英文)

作　　者：胡伍生[1,2] 孙璐[2] 

机构地区：[1]东南大学交通学院,南京210096 [2]美国华盛顿天主教大学土木工程学院,华盛顿20064

出　　处：《Journal of Southeast University(English Edition)》2009年第3期400-403,共4页东南大学学报（英文版）

基　　金：The National Basic Research Program of China(973 Program)(No.2006CB705501);the National High Technology Research and Development Program of China (863 Program)(No.2007AA12Z228)

摘　　要：Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network （called the H-BP algorithm） for compensating function model errors is put forward. The function model is assumed as y =f（x1, x2,… ,xn）, and the special structure of the H-BP algorithm is determined as （ n ＋ 1） ×p × 1, where （n ＋ 1） is the element number of the input layer, and the elements are xl, x2,…, xn and y＇ （ y＇ is the value calculated by the function model）; p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y＇（y0 is the known value of the sample）. The calculation steps of the H-BP algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-BP algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors.介绍了2种补偿模型误差的传统方法:附加系统参数方法和最小二乘配置法.提出了一种基于BP算法的补偿模型误差的神经网络方法,简称为H-BP算法.假设函数模型为y=f(x1,x2,…,xn),则H-BP算法的神经网络结构为(n+1)×p×1,(n+1)是输入层元素个数,具体为x1,x2,…,xn和y′,其中y′是函数模型计算值;p为隐含层节点数,一般通过大量试验得到;1是输出层元素个数,具体为Δy=y0-y′,其中y0是样本真值.然后,详细介绍了H-BP算法的具体计算步骤.最后,结合一个工程实例,对3种补偿方法的结果进行了详细对比分析.传统方法补偿之后的精度约为±19mm,H-BP算法补偿之后的精度为±4.3mm.结果表明,与传统方法相比,新方法对模型误差的补偿效果更好.

关 键 词：model error neural network BP algorithm compen- sating 

分 类 号：P207[天文地球—测绘科学与技术]