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摘要:
Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness;the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.
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篇名 Probabilistic Fuzzy Regression Approach from the Point of View Risk
来源期刊 数据分析和信息处理(英文) 学科 数学
关键词 PROBABILISTIC Fuzzy Regression Chaos Optimization Algorithm RISK PREFERENCES Models Mean ABSOLUTE PERCENTAGE Error Variance of ERRORS
年,卷(期) sjfxhxxclyw_2018,(4) 所属期刊栏目
研究方向 页码范围 156-167
页数 12页 分类号 O1
字数 语种
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研究主题发展历程
节点文献
PROBABILISTIC
Fuzzy
Regression
Chaos
Optimization
Algorithm
RISK
PREFERENCES
Models
Mean
ABSOLUTE
PERCENTAGE
Error
Variance
of
ERRORS
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
数据分析和信息处理(英文)
季刊
2327-7211
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
106
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0
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