Abstract
A method for hybrid least-squares regression, based on the weighted fuzzy arithmetic and the least-squares fitting criterion, is developed in this chapter. Both bivariate regression model and multiple regression model are derived and developed. Two numerical examples are used to demonstrate the proposed method. In each example, hybrid regression equations and their reliability measures are calculated. Furthermore, hybrid least-squares regression is extended to nonlinear models. At the end, conclusions are drawn based on the reliability evaluations.
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References
Ayyub, B.M., and McCuen, R. (1996). Numerical Methods for Engineers. Prentice Hall, New York, New York.
Chang, Yun-Hsi O. (1996). Hybrid Regression Analysis with Reliability and Uncertainty Measures. Ph. D. Dissertation, University of Maryland, at College Park, Maryland.
Draper, N., and Smith, H. (1981). Applied Regression Analysis, second edition. John Wiley & Sons, Inc., New York, New York.
Kreyzig, Erwin (1993). Advanced Engineering Mathematics, seventh edition. John Wiley & Sons, Inc., New York, New York.
Tanaka, H. and Uejima, S., and Asai, K. (1982). Linear Regression Analysis with Fuzzy Model,“ IEEE, Systems, Transactions on Systems, Man, and Cybernetics, SMC-2(6), 903–907.
Younger, Mary Sue (1979). Handbook for Linear Regression. Duxbury Press, Belmont, California.
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Chang, YH.O., Ayyub, B.M. (1998). Hybrid Least-Squares Regression Analysis. In: Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach . International Series in Intelligent Technologies, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5473-8_12
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DOI: https://doi.org/10.1007/978-1-4615-5473-8_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7500-5
Online ISBN: 978-1-4615-5473-8
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