全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Probabilistic Fuzzy Regression Approach from the Point of View Risk

DOI: 10.4236/jdaip.2018.64010, PP. 156-167

Keywords: Probabilistic Fuzzy Regression, Chaos Optimization Algorithm, Risk Preferences Models, Mean Absolute Percentage Error, Variance of Errors

Full-Text   Cite this paper   Add to My Lib

Abstract:

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.

References

[1]  Tanaka, H. (1982) Linear Regression Analysis with Fuzzy Model. IEEE Transactions on Systems, Man, and Cybernetics, 12, 903-907.
https://doi.org/10.1109/TSMC.1982.4308925
[2]  Diamond, P. (1988) Fuzzy Least Squares. Information Sciences, 46, 141-157.
https://doi.org/10.1016/0020-0255(88)90047-3
[3]  Tanaka, H. and Watada, J. (1988) Possibilistic Linear Systems and Their Application to the Linear Regression Model. Fuzzy Sets & Systems, 27, 275-289.
https://doi.org/10.1016/0165-0114(88)90054-1
[4]  Dragon, A. (1991) Evaluation of Fuzzy Linear Regression Models. Fuzzy Sets & Systems, 35, 51-63.
[5]  Kim, B. and Bishu, R.R. (1998) Evaluation of Fuzzy Linear Regression Models by Comparing Membership Functions. Fuzzy Sets & Systems, 100, 343-352.
https://doi.org/10.1016/S0165-0114(97)00100-0
[6]  Modarres, M., Nasrabadi, E. and Nasrabadi, M.M. (2004) Fuzzy Linear Regression Analysis from the Point of View Risk. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 12, 635-649.
https://doi.org/10.1142/S0218488504003120
[7]  Wong, C.K. and Chen, K.Y. (2010) A Generalized Fuzzy Least-Squares Regression Approach to Modeling Relationship in QFD. Journal of Engineering Design, 21, 601-603. https://doi.org/10.1080/09544820802563234
[8]  Zhang, A.W. (2016) Statistical Analysis of Fuzzy Linear Regression Model Based on Centroid Method. Applied Mathematics, 7, 579-586.
[9]  Li, J.H., Zeng, W.Y. and Xie, J.J. (2016) A New Fuzzy Regression Model Based on Least Absolute Deviation. Engineering Applications of Artificial Intelligence, 52, 54-64. https://doi.org/10.1016/j.engappai.2016.02.009
[10]  Hu, B.Q. (2010) Fuzzy Throry Basis. 2nd Edition, Wuhan University Press, Wuhan.
[11]  Li, B. and Jiang, W.S. (1997) Chaos Optimization Algorithm and Its Application. Control Theory and Application, 4, 613-615.
[12]  Nishikant, M., Choudhary, K.A. and Tiwari, M.K. (2008) Modeling the Planning and Scheduling across the Outsourcing Supply Chain: A Chaos-Based Fast Tabu—SA Approach. International Journal of Production Research, 46, 3683-3715.
[13]  Nanba, R., Hasegawa, M. and Nishita, T. (2002) Optimization Using Chaotic Neural Network and Its Application to Lighting Design. Control & Cybernetics, 2, 249-269.
[14]  Tanaka, H. and Hayshi, I. (1989) Possibilistic Linear Regression Analysis for Fuzzy Data. European Journal of Operational Research, 40, 389-396.
https://doi.org/10.1016/0377-2217(89)90431-1
[15]  Kao, C.A. and Chyu, C.-L. (2008) Least-Squares Estimates in Fuzzy Regression Analysis. European Journal of Operational Research, 148, 426-435.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133