Skip to main content
SpringerLink
Log in

Optimal Parameter Ranges in Fuzzy Inference Systems, Applied to Spatial Data

  • Conference paper
  • First Online:
Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 643))

  • 732 Accesses

Abstract

Processing of spatial data can benefit from the use of fuzzy inference systems, and such systems have been proposed to deal with the map overlay problem for gridded data. The development of fuzzy inference system for solving spatial problems poses specific challenges due to the type of data and specific properties of the spatial context. In this contribution, we take into account that a spatial dataset can exhibit a big variety in different areas and determine the most possible ranges for the variables in the rulebase system in a more appropriate and dynamic way. In addition, we show how the construction and application of a rulebase can be modified in order to handle this changed definition of the most possible ranges.

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 EPUB and 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gotway, C.A., Young, L.J.: Combining incompatible spatial data. J. Am. Stat. Assoc. 97(458), 632–648 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Horabik, J., Nahorski, Z.: Improving resolution of a spatial air pollution inventory with a statistical inference approach. Climatic Change 124, 575–589 (2014)

    Article  Google Scholar 

  3. Hryniewicz, O., Nahorski, Z., Verstraete, J., Horabik, J., Jonas, M.: Compliance for uncertain inventories via probabilistic/fuzzy comparison of alternatives. Climatic Change 124(3), 519–534 (2014)

    Article  Google Scholar 

  4. Martinez-Urtaza, J., Bowers, J.C., Trinanes, J., DePaola, A.: Climate anomalies and the increasing risk of vibrio parahaemolyticus and vibrio vulnificus illnesses. Food Res. Int. 43(7), 1780–1790 (2010)

    Article  Google Scholar 

  5. Mugglin, A.S., Carlin, B.P., Gelfand, A.E.: Fully model-based approaches for spatially misaligned data. J. Am. Stat. Assoc. 95(451), 877–887 (2000)

    Article  Google Scholar 

  6. Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases with Applications to GIS. Morgan Kaufman Publishers, San Francisco (2002)

    Google Scholar 

  7. Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Pearson Educations, Upper Saddle River (2003)

    Google Scholar 

  8. Tobler, W.R.: Smooth pycnophylactic interpolation for geographic regions. J. Am. Stat. Assoc. 74(367), 519–536 (1979)

    Article  MathSciNet  Google Scholar 

  9. Verstraete, J.: Solving the map overlay problem with a fuzzy approach. Climatic Change 124, 591–604 (2014)

    Article  Google Scholar 

  10. Verstraete, J.: The spatial disaggregation problem: simulating reasoning using a fuzzy inference system. IEEE Trans. Fuzzy Syst. PP(99), 1 (2016)

    Google Scholar 

  11. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work has received financial support from the Consellera de Cultura, Educacin e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF).

Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warszawa, Poland

    Jörg Verstraete & Weronika Radziszewska

  2. Centro Singular de Investigación en Tecnoloxías da Informació, University of Santiago de Compostela, Rúa de Jenaro de la Fuente Domínguez, 15782, Santiago de Compostela, Spain

    Jörg Verstraete

Authors
  1. Jörg Verstraete
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weronika Radziszewska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jörg Verstraete .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Institute of Biophysics and Biomedical Engineering, Department of Bioinformatics and Mathematical Modelling, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Verstraete, J., Radziszewska, W. (2018). Optimal Parameter Ranges in Fuzzy Inference Systems, Applied to Spatial Data. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-319-66827-7_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66827-7_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66826-0

  • Online ISBN: 978-3-319-66827-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation