Skip to main content
SpringerLink
Log in

Neuro-Fuzzy Systems

  • Conference paper
Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications

Part of the book series: NATO ASI Series ((NATO ASI F,volume 162))

Abstract

This paper is about so-called neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper definition, and still has the flavour of a buzzword to it. In this paper we try to give it a meaning in the context of three applications of fuzzy systems, which are fuzzy control, fuzzy classification, and fuzzy function approximation.

Surprisingly few neuro-fuzzy approaches do actually employ neural networks, even though they are very often depicted in form of some kind of neural network structure. However, all approaches display some kind of learning capability, as it is known from neural networks. This means, they use algorithms which enable them to determine their parameters from training data in an iterative process. From our point of view neuro-fuzzy means using heuristic learning strategies derived from the domain of neural network theory to support the development of a fuzzy system.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  1. Igor Aleksander and Helena Morton. An Introduction to Neural Computing. Chapman & Hall, London, 1990.

    Google Scholar 

  2. Andrew G. Barto, Richard S. Sutton, and Charles W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Systems, Man & Cybernetics, 13:834–846, 1983.

    Article  Google Scholar 

  3. Hamid R. Berenji. A reinforcement learning-based architecture for fuzzy logic control. Int. J. Approximate Reasoning, 6:267–292, February 1992.

    Article  MATH  Google Scholar 

  4. Hamid R. Berenji and Pratap Khedkar. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Networks, 3:724–740, September 1992.

    Article  Google Scholar 

  5. Hugues Bersini, Jean-Pierre Nordvik, and Andrea Bonarini. A simple direct adaptive fuzzy controller derived from its neural equivalent. In Proc. IEEE Int. Conf. on Fuzzy Systems 1993, pages 345–350, San Francisco, March 1993.

    Google Scholar 

  6. James C. Bezdek, Eric Chen-Kuo Tsao, and Nikhil R. Pal. Fuzzy Kohonen clustering networks. In Proc. IEEE Int. Conf. on Fuzzy Systems 1992, pages 1035–1043, San Diego, CA, 1992.

    Google Scholar 

  7. J. J. Buckley. Sugeno type controllers are universal controllers. Fuzzy Sets and Systems, 53:299–303, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  8. James J. Buckley and Yoichi Hayashi. Fuzzy neural networks: A survey. Fuzzy Sets and Systems, 66:1–13, 1994.

    Article  MathSciNet  Google Scholar 

  9. James J. Buckley and Yoichi Hayashi. Neural networks for fuzzy systems. Fuzzy Sets and Systems, 71:265–276, 1995.

    Article  MathSciNet  Google Scholar 

  10. Gail A. Carpenter, Stephen Grossberg, Natalya Markuzon, John H. Reynolds, and David B. Rosen. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Networks, 3(5):698–712, September 1992.

    Article  Google Scholar 

  11. R.A. Fisher. The use of multiple measurements in taxonomic problems. Annual Eugenics, 7(Part II):179–188, 1936.

    Google Scholar 

  12. Saman K. Halgamuge and Manfred Glesner. Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems, 65:1–12, 1994.

    Article  Google Scholar 

  13. Simon Haykin. Neural Networks. A Comprehensive Foundation. Macmillan College Publishing Company, New York, 1994.

    MATH  Google Scholar 

  14. M. Hornik, M. Stinchcombe, and H. White. Multilayer feedfoward networks are universal approximators. Neural Networks, 2:359–366, 1989.

    Article  Google Scholar 

  15. J. S. Roger Jang. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Systems, Man Sr Cybernetics, 23:665–685, 1993.

    Article  Google Scholar 

  16. James M. Keller and Hossein Tahani. Backpropagation neural networks for fuzzy logic. Information Sciences, 62:205–221, 1992.

    Article  Google Scholar 

  17. F. Klawonn and R. Kruse. Fuzzy control on the basis of equality relations with an example from idle speed control. IEEE Trans. Fuzzy Systems, pages 336–350, 1995.

    Google Scholar 

  18. Bart Kosko. Fuzzy systems as universal approximators. In Proc. IEEE Int. Conf. on Fuzzy Systems 1992, pages 1153–1162, San Diego, CA, March 1992.

    Google Scholar 

  19. Bart Kosko. Neural Networks and Fuzzy Systems. A Dynamical Systems Ap- proach to Machine Intelligence. Prentice-Hall, Englewood Cliffs, NJ, 1992.

    Google Scholar 

  20. Rudolf Kruse, Jörg Gebhardt, and Frank Klawonn. Foundations of Fuzzy Systems. Wiley, Chichester, 1994.

    Google Scholar 

  21. Chuen Chien Lee. Fuzzy logic in control systems: Fuzzy logic controller, part i. IEEE Trans. Systems, Man & Cybernetics, 20:404–418, 1990.

    Article  MATH  Google Scholar 

  22. Chuen Chien Lee. Fuzzy logic in control systems: Fuzzy logic controller, part ii. IEEE Trans. Systems, Man & Cybernetics, 20:419–435, 1990.

    Article  MATH  Google Scholar 

  23. E. H. Mamdani and S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Machine Studies, 7:1–13, 1975.

    Article  MATH  Google Scholar 

  24. Sushmita Mitra and Ludmilla Kuncheva. Improving classification performance using fuzzy mlp and two-level selective partitioning of the feature space. Fuzzy Sets and Systems, 70:1–13, 1995.

    Article  Google Scholar 

  25. Detlef Nauck, Frank Klawonn, and Rudolf Kruse. Foundations of Neuro-Fuzzy Systems. Wiley, Chichester, 1997.

    Google Scholar 

  26. Detlef Nauck and Rudolf Kruse. NEFCON-I: An X-Window based simulator for neural fuzzy controllers. In Proc. IEEE hit. Conf. Neural Networks 1994 at IEEE WCCI’94, pages 1638–1643, Orlando, FL, June 1994.

    Google Scholar 

  27. Detlef Nauck and Rudolf Kruse. NEFCLASS - a neuro-fuzzy approach for the classification of data. In K. M. George, Janice H. Carrol, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Applied Computing 1995. Proc. 1995 ACM Symposium on Applied Computing,Nashville, Feb. 26–28, pages 461–465. ACM Press, New York, February 1995.

    Chapter  Google Scholar 

  28. Detlef Nauck and Rudolf Kruse. Designing neuro-fuzzy systems through back-propagation. In Witold Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, pages 203–228. Kluwer, Boston, 1996.

    Chapter  Google Scholar 

  29. Detlef Nauck and Rudolf Kruse. Neuro-fuzzy systems research and applications outside of Japan (in japanese). In M. Umano, I. Hayashi, and T. Furuhashi, editors, Fuzzy-Neural Networks (in Japanese), Soft Computing Series, pages 108–134. Asakura Publ., Tokyo, 1996.

    Google Scholar 

  30. Detlef Nauck and Rudolf Kruse. Neuro-fuzzy systems for function approximation. In Adolf Grauel, Wilhelm Becker, and Fevzi Belli, editors, Fuzzy-NeuroSysteme’97 - Computational Intelligence. Proc. 4th Int. Workshop FuzzyNeuro-Systeme ‘87 (FNS’97) in Soest, Germany, Proceedings in Artificial Intelligence, pages 316–323, Sankt Augustin, 1997. infix.

    Google Scholar 

  31. Hiroyoshi Nomura, Isao Hayashi, and Noboru Wakami. A learning method of fuzzy inference rules by descent method. In Proc. IEEE Int. Conf. on Fuzzy Systems 1992, pages 203–210, San Diego, CA, 1992.

    Google Scholar 

  32. Ann Nowé and Ranjan Vepa. A reinforcement learning algorithm based on ‘safety’. In Erich Peter Klement and Wolfgang Slany, editors, Fuzzy Logic in Artificial Intelligence (FLAI93), pages 47–58, Berlin, 1993. Springer-Verlag.

    Chapter  Google Scholar 

  33. S. K. Pal and S. Mitra. Multi-layer perceptron, fuzzy sets and classification. IEEE Trans. Neural Networks, 3:683–697, 1992.

    Article  Google Scholar 

  34. Witold Pedrycz and H. C. Card. Linguistic interpretation of self-organizing maps. In Proc. IEEE Int. Conf. on Fuzzy Systems 1992, pages 371–378, San Diego, CA, 1992.

    Google Scholar 

  35. T. Poggio and F. Girosi. A theory of networks for approximation and learning. A.I. Memo 1140, MIT, 1989.

    Google Scholar 

  36. P. K. Simpson. Fuzzy min-max neural networks - part 1: Classification. IEEE Trans. Neural Networks, 3:776–786, 1992.

    Article  Google Scholar 

  37. P. K. Simpson. Fuzzy min-max neural networks - part 2: Clustering. IEEE Trans. Fuzzy Systems, 1:32–45, February 1992.

    Article  Google Scholar 

  38. M. Sugeno. An introductory survey of fuzzy control. Information Sciences, 36:59–83, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  39. Nadine Tschichold-Gürman. Generation and improvement of fuzzy classifiers with incremental learning using fuzzy rulenet. In K. M. George, Janice H. Carrot, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Applied Computing 1995. Proc. 1995 ACM Symposium on Applied Computing,Nashville, Feb. 26–28, pages 466–470. ACM Press, New York; February 1995.

    Chapter  Google Scholar 

  40. Nadine Tschichold-Gürman. RuleNet - A New Knowledge-Based Artificial Neural Network Model with Application Examples in Robotics. PhD thesis, ETH Zürich, 1996.

    Google Scholar 

  41. Petri Vuorimaa. Fuzzy self-organizing map. Fuzzy Sets and Systems, 66:223–231,1994.

    Article  Google Scholar 

  42. David A. White and Donald A. Sofge, editors. Handbook of Intelligent Control. Neural, Fuzzy, and Adaptive Approaches. Van Nostrand Reinhold, New York, 1992.

    Google Scholar 

  43. Jacek M. Zurada. Introduction to Artificial Neural Systems. West Publishing Company, St. Paul, MN, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty Of Computer Science, Otto-von-Guericke-University of Magdeburg, Universitätsplatz 2, Magdeburg, D-39106, Germany

    Rudolf Kruse & Detlef Nauck

Authors
  1. Rudolf Kruse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Detlef Nauck
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Bogaziçi University, Istanbul, TR-80815, Turkey

    Okyay Kaynak

  2. Computer Science Division, University of California at Berkeley, Berkeley, CA, 94720-1776, USA

    Lotfi A. Zadeh

  3. University of Toronto, Ontario, M5S 3G8, Canada

    Burhan Türkşen

  4. Bánki Donát Polytechnic, Népszinház u. 8, Budapest, H-1081, Hungary

    Imre J. Rudas

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kruse, R., Nauck, D. (1998). Neuro-Fuzzy Systems. In: Kaynak, O., Zadeh, L.A., Türkşen, B., Rudas, I.J. (eds) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. NATO ASI Series, vol 162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58930-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-58930-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63796-4

  • Online ISBN: 978-3-642-58930-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation