Skip to main content
SpringerLink
Log in

Neuro-fuzzy Systems with Relation Matrix

  • Conference paper
Book cover Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

Abstract

Neuro-fuzzy systems are eagerly used for classification and machine learning problems. Researchers find them easy to use because the knowledge is stored in the form of the fuzzy rules. The rules are relatively easy to create and interpret for humans, unlike in the case of other learning paradigms e.g. neural networks. The most commonly used neuro-fuzzy systems are Mamdani (linguistic) and Takagi Sugeno systems. There are also logical-type systems which are well suited for classification tasks. In the paper, another type of fuzzy systems is proposed, i.e. multi-input multi-output systems with additional binary relation for greater flexibility. The relation bonds input and output fuzzy linguistic values. Thanks to this, the system is better adjustable to learning data. The systems have multiple outputs which is crucial in the case of classification tasks. Described systems are tested on several known benchmarks and compared with other machine learning solutions from the literature.

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. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

  2. Babuska, R.: Fuzzy Modeling For Control. Kluwer Academic Press, Boston (1998)

    Google Scholar 

  3. Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition. IEEE Press, New York (1992)

    Google Scholar 

  4. Bezdek, J., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Press, Dordrecht (1999)

    MATH  Google Scholar 

  5. Branco, P.J.C., Dente, J.A.: A Fuzzy Relational identification Algorithm and its Application to Predict the Behaviour of a Motor Drive System. Fuzzy Sets and Systems 109, 343–354 (2000)

    Article  MATH  Google Scholar 

  6. Dong, M., Kothari, R.: Look-ahead based fuzzy decision tree induction. IEEE Trans. on Fuzzy Systems 9, 461–468 (2001)

    Article  Google Scholar 

  7. Ischibuchi, H., Nakashima, T.: Effect of Rule Weights in Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems 9(4), 506–515 (2001)

    Article  Google Scholar 

  8. Jang, R.J.-S., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)

    Google Scholar 

  9. Nauck, D., Klawon, F., Kruse, R.: Foundations of Neuro - Fuzzy Systems. John Wiley, Chichester (1997)

    Google Scholar 

  10. Nauck, D., Kruse, R.: How the Learning of Rule Weights Affects the Interpretability of Fuzzy Systems. In: Proceedings of 1998 IEEE World Congress on Computational Intelligence, FUZZ-IEEE, Alaska, pp. 1235–1240 (1998)

    Google Scholar 

  11. Nozaki, K., Ishibuchi, H., Tanaka, K.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270 (1995)

    Article  Google Scholar 

  12. Pedrycz, W.: Fuzzy Control and Fuzzy Systems. Research Studies Press, London (1989)

    MATH  Google Scholar 

  13. Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets, Analysis and Design. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  14. Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Dordrecht (2004)

    MATH  Google Scholar 

  15. Scherer, R., Rutkowski, L.: Relational Equations Initializing Neuro-Fuzzy System. In: 10th Zittau Fuzzy Colloquium, Zittau, Germany (2002)

    Google Scholar 

  16. Scherer, R., Rutkowski, L.: Neuro-Fuzzy Relational Systems. In: 2002 International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, November 18-22 (2002)

    Google Scholar 

  17. Setness, M., Babuska, R.: Fuzzy Relational Classifier Trained by Fuzzy Clustering. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 29(5), 619–625 (1999)

    Article  Google Scholar 

  18. Sugeno, M., Yasukawa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)

    Article  Google Scholar 

  19. Wang, L.-X.: Adaptive Fuzzy Systems And Control. PTR Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  20. Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control. John Wiley & Sons Inc., New York (1994)

    Google Scholar 

  21. Yager, R.R., Filev, D.P.: On a Flexible Structure for Fuzzy Systems Models. In: Yager, R.R., Zadeh, L.A. (eds.) Fuzzy Sets, Neural Networks and Soft Computing, Van Nostrand Reinhold, New York, pp. 1–28 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Academy of Management (SWSPiZ), Institute of Information Technology, ul. Sienkiewicza 9, 90-113, Łódź, Poland

    Rafał Scherer

  2. Department of Computer Engineering, Czȩstochowa University of Technology, al. Armii Krajowej 36, 42-200, Czȩstochowa, Poland

    Rafał Scherer

Authors
  1. Rafał Scherer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Scherer, R. (2010). Neuro-fuzzy Systems with Relation Matrix. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation