Skip to main content
SpringerLink
Log in

Fuzzy Regression Approaches and Applications

  • Chapter
Fuzzy Applications in Industrial Engineering

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 201))

Abstract

Fuzzy regression is a fuzzy variation of classical regression analysis. It has beenstudied and applied to various areas. Two types of fuzzy regression models are Tanaka’s linear programming approach and the fuzzy least-squares approach. In this chapter, a wide literature review including both theoretical and application papers on fuzzy regression has been given. Fuzzy regression models for nonfuzzy input/nonfuzzy output, nonfuzzy input/fuzzy output, and possibilistic regression model have been summarized. An illustrative example has been given. Fuzzy hypothesis testing for the coefficients of a linear regression function has been explained with two numerical examples.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bell, P.M., Wang, H., Fuzzy linear regression models for assessing risks of cumulative trauma disorders, Fuzzy Sets and Systems, Vol. 92, pp. 317–340, 1997.

    Article  MathSciNet  Google Scholar 

  • Buckley, J.J., Fuzzy Statistics, Springer-Verlag, Berlin, 2004.

    MATH  Google Scholar 

  • Buckley, J.J., Feuring, T., Linear and non-linear fuzzy regression: Evolutionary algorithm solutions, Fuzzy Sets and Systems, Vol. 112, pp. 381–394, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  • Chang, P-T., Fuzzy seasonality forecasting, Fuzzy Sets and Systems, Vol. 90, pp. 1–10, 1997.

    Article  Google Scholar 

  • Chang, Y-H. O., Hybrid fuzzy least-squares regression analysis and its reliability measures, Fuzzy Sets and Systems, Vol. 119, pp. 225–246, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  • Chang, Y-H. O., Ayyub, B.M., Fuzzy regression methods – a comparative assessment, Fuzzy Sets and Systems, Vol. 119, pp. 187–203, 2001.

    Article  MathSciNet  Google Scholar 

  • Chen, Y-S., Fuzzy ranking and quadratic fuzzy regression, Computers & Mathematics with Applications, Vol. 38, pp. 265–279, 1999.

    Article  MATH  Google Scholar 

  • Chen, Y-S., Outliers detection and confidence interval modification in fuzzy regression, Fuzzy Sets and Systems, Vol. 119, pp. 259–272, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  • Cheng, C.-B., Lee, E.S., Nonparametric fuzzy regression–k-NN and kernel smoothing techniques, Computers & Mathematics with Applications, Vol. 38, pp. 239–251, 1999a.

    Article  MATH  MathSciNet  Google Scholar 

  • Cheng, C.-B., Lee, E.S., Applying fuzzy adaptive network to fuzzy regression analysis, Computers & Mathematics with Applications, Vol. 38, pp. 123–140, 1999b.

    Article  MATH  MathSciNet  Google Scholar 

  • Cheng, C-B., Lee, E.S., Fuzzy regression with radial basis function network, Fuzzy Sets and Systems, Vol. 119, pp. 291–301, 2001.

    Article  MathSciNet  Google Scholar 

  • Diamond, P., Fuzzy least squares, Information Sciences, Vol. 46, pp. 141–157, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  • Dunyak, J.P., Wunsch, D., Fuzzy regression by fuzzy number neural networks, Fuzzy Sets and Systems, Vol. 112, pp. 371–380, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  • D’Urso, P., Gastaldi, T., A least-squares approach to fuzzy linear regression analysis, Computational Statistics & Data Analysis, Vol. 34, pp. 427–440, 2000.

    Article  MATH  Google Scholar 

  • D’Urso, P., Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data, Computational Statistics & Data Analysis, Vol. 42, pp. 47–72 , 2003.

    Article  MathSciNet  Google Scholar 

  • Hestmaty, B., Kandel, A., Fuzzy linear regression and its applications to forecasting in uncertain environment, Fuzzy Sets and Systems, Vol.15, pp. 159–191, 1985.

    Article  Google Scholar 

  • Hojati, M., Bector, C.R., Smimou, K., A simple method for computation of fuzzy linear regression, European Journal of Operational Research, Volume 166, pp. 172–184 , 2005.

    Article  MATH  MathSciNet  Google Scholar 

  • Hong, D.H., Hwang, C., Extended fuzzy regression models using regularization method, Information Sciences, Vol. 164, 2004, pp. 31–46, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  • Höppner, F., Klawonn, F., Improved fuzzy partitions for fuzzy regression models, International journal of approximate reasoning, Vol. 32, pp. 85–102, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  • Ishibuchi, H., Nii, M., Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks, Fuzzy Sets and Systems, Vol. 119, pp. 273–290, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  • Kao, C., Chyu, C-L., A fuzzy linear regression model with better explanatory power, Fuzzy Sets and Systems, Vol. 126, pp. 401–409, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  • Kao, C., Chyu, C-L., Least-squares estimates in fuzzy regression analysis, European Journal of Operational Research, Vol. 148, pp. 426–435, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  • Kim, B., Bishu, R.R., Evaluation of fuzzy linear regression models by comparing membership functions, Fuzzy Sets and Systems, Vol. 100, pp. 343–352, 1998.

    Article  Google Scholar 

  • Ip, K.W., Kwong, C.K., Wong, Y.W., Fuzzy regression approach to modelling transfer moulding for microchip encapsulation, Journal of Materials Processing Technology, Vol. 140, pp. 147–151, 2003.

    Article  Google Scholar 

  • Lee, H.T., Chen, S.H., Fuzzy regression model with fuzzy input and output data for manpower forecasting, Fuzzy Sets and Systems, Vol. 119, pp. 205–213, 2001.

    Article  MathSciNet  Google Scholar 

  • Nasrabadi, M.M., Nasrabadi, E., A mathematical-programming approach to fuzzy linear regression analysis, Applied Mathematics and Computation, Vol. 155, pp. 873–881, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  • Nasrabadi, M.M., Nasrabadi, E., Nasrabady, A.R., Fuzzy linear regression analysis: a multi-objective programming approach, Applied Mathematics and Computation, Vol. 163, pp. 245–251, 2005.

    Article  MATH  MathSciNet  Google Scholar 

  • Özelkan, E.C., Duckstein, L., Multi-objective fuzzy regression: a general framework, Computers & Operations Research, Vol. 27, pp. 635–652, 2000.

    Article  MATH  Google Scholar 

  • Sánchez, J.A., Gómez, A.T., Estimating a term structure of interest rates for fuzzy financial pricing by using fuzzy regression methods, Fuzzy Sets and Systems, Vol. 139, pp. 313–331, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  • Sánchez, J.A., Gómez, A.T., Estimating a fuzzy term structure of interest rates using fuzzy regression techniques, European Journal of Operational Research, Vol. 154, pp. 804–818, 2004.

    Article  MATH  Google Scholar 

  • Shapiro, A. F., Fuzzy regression and the term structure of interest rates revisited, AFIR2004.

    Google Scholar 

  • Soliman, S.A., Alammari, R.A., El-Hawary, M.E., Frequency and harmonics evaluation in power networks using fuzzy regression technique, Electric Power Systems Research, Vol. 66, pp. 171–177, 2003.

    Article  Google Scholar 

  • Tanaka, H. and Guo, P., Possibilistic Data Analysis for Operations Research, Physica-Verlag, 1999.

    Google Scholar 

  • Tanaka, H., Uejima, S., Asai, K., Fuzzy linear regression model, IEEE Transactions on Systems Man Cybernetics, Vol. 12, pp. 903–907, 1982.

    Article  MATH  Google Scholar 

  • Tran, L., Duckstein, L., Multiobjective fuzzy regression with central tendecy and possibilistic properties, Fuzzy Sets and Systems, Vol. 130, pp. 21–31, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  • Tseng, F-M., Tzeng, G-H., Yu, H-C., Yuan, B.J.C.,Fuzzy ARIMA model for forecasting the foreign exchange market, Fuzzy Sets and Systems, Vol. 118, pp. 9–19, 2001.

    Article  MathSciNet  Google Scholar 

  • Tseng, F-M., Tzeng, G-H., A fuzzy seasonal ARIMA model for forecasting, Fuzzy Sets and System, Vol. 126, pp. 367–376, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  • Tseng, F-M., Tzeng, G-H., Yu, H-C., Fuzzy seasonal time series for forecasting the production value of the mechanical industry in Taiwan, Technological Forecasting and Social Change, Vol. 60, pp. 263–273, 1999.

    Article  Google Scholar 

  • Yang, M-S., Lin, T-S., Fuzzy least-squares linear regression nalysis for fuzzy input- output data, Fuzzy Sets and Systems, Vol. 126, pp. 389–399, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  • Yang M-S., Liu, H-H., Fuzzy least-squares algorithms for interactive fuzzy linear regression models, Fuzzy Sets and Systems, Vol. 135, pp. 305–316, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  • Yen, K.K., Ghoshray, S., Roig, G., A linear regression model using triangular fuzzy number coefficients, Fuzzy Sets and Systems, Vol. 106, pp. 167–177, 1999.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Maçka, Istanbul, 34367, Turkey

    Cengiz Kahraman

  2. Engineering Faculty, Department of Industrial Engineering, Bahçeşehir University, Bahçeşehir, Istanbul, Turkey

    Ahmet Beşkese & F. Tunç Bozbura

Authors
  1. Cengiz Kahraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmet Beşkese
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Tunç Bozbura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Industrial Department of Engineering Faculty Management, Istanbul Technical University, Campus Macka, Istanbul, 80680, Turkey

    Cengiz Kahraman Dr.

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Kahraman, C., Beşkese, A., Bozbura, F.T. (2006). Fuzzy Regression Approaches and Applications. In: Kahraman, C. (eds) Fuzzy Applications in Industrial Engineering. Studies in Fuzziness and Soft Computing, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33517-X_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-33517-X_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33516-0

  • Online ISBN: 978-3-540-33517-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation