地理空间信息2013，Vol.11Issue(1)：55-57,72,4.
基于模糊隶属函数的参数稳健估计
Robust Estimation of Parameters Based on Fuzzy Membership Function
摘要
Abstract
There are two major issues in the process of parameter estimation. The first one is that least-squares method and robust estimation method cannot give little consideration to the optimal unbiased and stabilized result. The second one is that model error makes it difficult to identify and position the gross error, which is introduced during the process of conversion from nonlinear function to a linear one. This article put forward a robust estimation method which based on the fuzzy membership function to eliminate the influence of gross error. The fuzzy membership of the residuals were used to construct the weight matrix to assess the contribution of each observation quantity, and then the one contaminated by gross error would be given very little weight to increase the accuracy of parameter estimation. This algorithm was assessed by estimating the parameters in linear regression model and nonlinear regression model contaminated by gross error. It is found that this algorithm is robust to the influence of gross error, and outperforms the common least-square algorithm to give more accurate result.关键词
模糊隶属函数/非线性模型/参数估计/最小二乘/稳健估计Key words
fuzzy membership functions,nonlinear model, parameter estimation,the least square algorithm,robust estimation分类
天文与地球科学引用本文复制引用
王永弟,丁海勇,罗海滨..基于模糊隶属函数的参数稳健估计[J].地理空间信息,2013,11(1):55-57,72,4.基金项目
江苏省高校自然科学研究项目(1KJB420002) （1KJB420002）
南京信息工程大学科研基金资助项目(S8110063001、20090207). （S8110063001、20090207）
