Optimizing h value for fuzzy linear regression with asymmetric triangular fuzzy coefficients
Optimizing h value for fuzzy linear regression with asymmetric triangular fuzzy coefficients
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DOI：
10.1016/j.engappai.2015.02.011
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Fangning Chen;Yizeng Chen;Jian Zhou;Yuanyuan Liu
中科院分区：
文献类型：
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作者：
Fangning Chen;Yizeng Chen;Jian Zhou;Yuanyuan Liu
模糊线性回归模型中的参数是非常重要的，因为它直接影响所估计的模糊线性关系对给定数据的拟合程度。然而，在实践中，它通常是由决策者主观地预先选择作为模型的输入。Liu and Chen（2013）将系统模糊度与系统隶属度相结合，引入了系统可信度的新概念，并提出了一种基于最小模糊度准则和对称三角模糊系数优化模糊线性回归分析h值的系统方法。作为推广，本文将他们的方法推广到非对称情形，并描述了求具有非对称三角模糊系数的模糊线性回归模型的最优H值以最大化系统可信度的过程。文中给出了具体的算例，并通过测试数据集进行了比较研究。
The parameterhin a fuzzy linear regression model is vital since it influences the degree of the fitting of the estimated fuzzy linear relationship to the given data directly. However, it is usually subjectively pre-selected by a decision-maker as an input to the model in practice. In Liu and Chen (2013), a new concept of system credibility was introduced by combining the system fuzziness with the system membership degree, and a systematic approach was proposed to optimize thehvalue for fuzzy linear regression analysis using the minimum fuzziness criterion with symmetric triangular fuzzy coefficients. As an extension, in this paper, their approach is extended to asymmetric cases, and the procedure to find the optimalhvalue to maximize the system credibility of the fuzzy linear regression model with asymmetric triangular fuzzy coefficients is described. Some illustrative examples are given to show the detailed procedure of this approach, and comparative studies are also conducted via the testing data sets.