摘要
针对统计学中基于线性回归分析的系统负荷准确预测问题,提出一种基于协方差改进稳健模糊回归的预测数学模型。通过引入协方差参数估计,对传统基于均值参数的稳健估计方程进行改进,以便降低对数据中异常点的敏感性,并对多元正态变量的渐近性质进行分析。结合模糊线性回归,将预测结果划分在一个合理的模糊区间,从而进一步排除异常点。实例测试结果表明,相比传统的模糊线性回归模型,提出的稳健模糊回归模型能够有效降低异常数据的影响,降低预测误差。
In allusion to accuracy problem of system load prediction based on linear regression analysis in statistics,a predictive mathematical model based on covariance improved robust fuzzy regression is proposed.The robust estimation equation based on mean parameter is improved by introducing covariance parameter estimation,so as to reduce the sensitivity to the abnormal points in the data.The asymptotic property of the multivariate normal variables is analyzed.The prediction results are divided into a reasonable fuzzy interval in combination with fuzzy linear regression,so as to further eliminate the abnormal points.The example test results show that in comparison with the traditional fuzzy linear regression model,the proposed robust fuzzy regression model can effectively reduce the influence of abnormal data and effectively reduce the prediction error.
作者
孙珍
SUN Zhen(Ankang University,Ankang 725000,China)
出处
《现代电子技术》
北大核心
2019年第15期94-96,100,共4页
Modern Electronics Technique
关键词
预测数学模型
线性回归分析
负荷预测
稳健估计
渐近性质分析
模糊回归
forecasting mathematical model
linear regression analysis
load forecasting
robust estimation
asymptotic property analysis
fuzzy regression
