摘要
基于最小二乘法准则的传统数据拟合方法对自变量为随机变量的向量数据拟合效果不理想。有鉴于此,本文提出几何距离平方和最小的新数据拟合标准,给出基于新标准下的新数据拟合方法,同时给出数据拟合参数求解的优化算法。仿真实验表明,在用于自变量为随机变量的向量数据拟合时,用新数据拟合方法的拟合精度比用最小二乘法的拟合精度要高。
The traditional data fitting method based on least square method is not good for vector data fitting whose independent variable is random. So this paper proposes a new criterion of data fitting which is the least quadratic sum of geometrical distance, and brings forward the new data fitting method based on the new criterion. At the same time the paper puts forward the optimization algorithm for the solution of the data fitting parameter. Simulation experiments show that the fitting precision of the new method is higher than the one of least square method for data fitting of vector, whose independent variable is random.
出处
《微计算机信息》
北大核心
2006年第08X期151-152,163,共3页
Control & Automation
基金
国家自然科学基金项目(60375014)
中国博士后科学基金项目
关键词
数据拟合
几何距离
最小二乘法
data fitting, geometrical distance, least square method
