Skip to main content
SpringerLink
Log in
Book cover

Developments in Soft Computing pp 175–182Cite as

Symbolic and Numerical Regression: Experiments and Applications

  • Conference paper

Part of the book series: Advances in Soft Computing ((AINSC,volume 9))

Summary

This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook-White equation and rainfall-runoff modelling.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Draper, N. R., and H. Smith. 1998.Applied regression analysis. New York: John Wiley and Sons.

    MATH  Google Scholar 

  • Eeles, C. W. O. 1994. “Parameter optimization of conceptual hydrological models,” Ph.D. Thesis, Open University, Milton Keynes, UK.

    Google Scholar 

  • Francone, F. D. 1998.Discipulus Pro owner’s manual. Oakland: Register Machine Learning Technologies Inc.

    Google Scholar 

  • Koza, J. R. 1992.Genetic programming: on the programming of computers by means of natural selection. Cambridge, Massachusetts: MIT Press.

    Google Scholar 

  • Poyhonen, H. 0., and D. A. Savic. 1996.Symbolic regression using object-oriented genetic programming (in C++): report number 96/04. Exeter: Centre for systems and control engineering, University of Exeter.

    Google Scholar 

  • Savic, D. A., G. A. Walters and J. W. Davidson. 1999. “A genetic programming approach to rainfall-runoff modelling,”Water Resources Management, 13, pp. 219 - 231.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, University of Exeter, EX4 4QF, Exeter, UK

    J. W. Davidson, D. A. Savic & G. A. Walters

Authors
  1. J. W. Davidson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. A. Savic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. A. Walters
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, De Montfort University, The Gateway, LE1 9BH, Leicester, UK

    Robert John  & Ralph Birkenhead  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Davidson, J.W., Savic, D.A., Walters, G.A. (2001). Symbolic and Numerical Regression: Experiments and Applications. In: John, R., Birkenhead, R. (eds) Developments in Soft Computing. Advances in Soft Computing, vol 9. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1829-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1829-1_21

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1361-6

  • Online ISBN: 978-3-7908-1829-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation