摘要
现代科学研究已经由之前对问题定性的描述更多进化为精细化、数量化的分析,所以在数学模型的构建、优化过程中经常会遇到对研究数据的可视化处理。最小二乘(LS)拟合是出现比较早的一类拟合方法,移动最小二乘(MLS)拟合与之相比,能较好体现数据的奇异特性。通过实例对比,推荐非数学专业研究人员采用MLS做数据拟合。
Modem scientific research has changed from the qualitative description of the problem to the quantitative analysis. In the process of mathematical modeling and model optimization, the visual processing of the data is often encountered. The least square approximation is an earlier fitting method, and the moving least squares fitting can reflect the singular characteristics of data better. Through case studies, it is recommended that non mathematical researchers use MLS to do data fitting.
出处
《辽宁高职学报》
2018年第1期76-77,共2页
Journal of Liaoning Higher Vocational
关键词
数据拟合
移动最小二乘近似
基函数
权函数
data fitting
moving least square approximation
primary function
weight function
