Skip to main content
SpringerLink
Log in

Variable Precision Fuzzy Rough Sets Model in the Analysis of Process Data

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3641))

Abstract

This paper is concerned with describing and analyzing the control actions which are accomplished by a human operator, who controls a complex dynamic system. The decision model is expressed by means of a decision table with fuzzy attributes. Decision tables are generated by the fuzzification of crisp data, basing on a set of fuzzy linguistic values of the attributes. A T-similarity relation is chosen for comparing the elements of the universe. Fuzzy partitions of the universe with respect to condition and decision attributes are generated. The task of stabilization of the aircraft’s altitude performed by a pilot is considered as an illustrative example. The limit-based and mean-based variable precision fuzzy rough approximations are determined. The measure of u-approximation quality is used for evaluating the consistency of the human operator’s decision model, and assessing the importance of particular condition attributes in the control process.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bodjanova, S.: Approximation of Fuzzy Concepts in Decision Making. Fuzzy Sets and Systems 85, 23–29 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  2. Chen, S.M., Yeh, M.S., Hsiao, P.Y.: A Comparison of Similarity Measures of Fuzzy Values. Fuzzy Sets and Systems 72, 79–89 (1995)

    Article  MathSciNet  Google Scholar 

  3. Dubois, D., Prade, H.: Putting Rough Sets and Fuzzy Sets Together. [16], 203–232

    Google Scholar 

  4. Fernández Salido, J.M., Murakami, S.: Rough Set Analysis of a General Type of Fuzzy Data Using Transitive Aggregations of Fuzzy Similarity Relations. Fuzzy Sets and Systems 139, 635–660 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Greco, S., Matarazzo, B., Słowiński, R.: Rough Set Processing of Vague Information Using Fuzzy Similarity Relations. In: Calude, C.S., Paun, G. (eds.) Finite Versus Infinite — Contributions to an Eternal Dilemma, pp. 149–173. Springer, Heidelberg (2000)

    Google Scholar 

  6. Katzberg, J.D., Ziarko, W.: Variable Precision Extension of Rough Sets. Fundamenta Informaticae 27, 155–168 (1996)

    MATH  MathSciNet  Google Scholar 

  7. Lin, T.Y.: Coping with Imprecision Information — Fuzzy Logic. Downsizing Expo, Santa Clara Convention Center (1993)

    Google Scholar 

  8. Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Rough Sets in Analysis of Inconsistent Decision Tables. In: Rutkowski, L., Kacprzyk, J. (eds.) Advances in Soft Computing, pp. 304–309. Physica, Heidelberg (2003)

    Google Scholar 

  9. Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Rough Sets: Evaluation of Human Operator’s Decision Model. In: Sołdek, J., Drobiazgiewicz, L. (eds.) Artificial Intelligence and Security in Computing Systems, pp. 33–40. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  10. Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Fuzzy Rough Sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS(Journal Subline), vol. 3100, pp. 144–160. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Mieszkowicz-Rolka, A., Rolka, L.: Remarks on Approximation Quality in Variable Precision Fuzzy Rough Sets Model. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 402–411. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Mrózek, A.: Rough Sets in Computer Implementation of Rule-Based Control of Industrial Processes. [16], 19–31

    Google Scholar 

  13. Pawlak, Z.: AI and Inteligent Industrial Applications: The Rough Set Perspective. Cybernetics and Systems: An International Journal 31, 227–252 (2000)

    Article  MATH  Google Scholar 

  14. Peters, J.F., Skowron, A., Suraj, Z.: An Application of Rough Sets Methods in Control Design. Fundamenta Informaticae 43, 269–290 (2000)

    MATH  MathSciNet  Google Scholar 

  15. Radzikowska, A.M., Kerre, E.E.: A Comparative Study of Fuzzy Rough Sets. Fuzzy Sets and Systems 126, 137–155 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  16. Słowiński, R. (ed.): Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992)

    MATH  Google Scholar 

  17. Ziarko, W.: Variable Precision Rough Sets Model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Avionics and Control, Rzeszów University of Technology, ul. W. Pola 2, 35-959, Rzeszów, Poland

    Alicja Mieszkowicz-Rolka & Leszek Rolka

Authors
  1. Alicja Mieszkowicz-Rolka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leszek Rolka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2 Canada, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, P.O. Box, Poland

    Dominik Ślęzak

  2. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, P.R. China

    Guoyin Wang

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Marcin Szczuka

  4. Department of Computer Science, Brock University, St. Catharines, L2S 3A1, Ontario, Canada

    Ivo Düntsch

  5. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mieszkowicz-Rolka, A., Rolka, L. (2005). Variable Precision Fuzzy Rough Sets Model in the Analysis of Process Data. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_37

Download citation

  • DOI: https://doi.org/10.1007/11548669_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28653-0

  • Online ISBN: 978-3-540-31825-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation