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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2004: Artificial Intelligence and Soft Computing - ICAISC 2004 pp 302–307Cite as

Fuzzy Number-Based Hierarchical Fuzzy System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

Abstract

Hierarchical fuzzy systems allow for reducing number of rules and for prioritization of rules. To retain fuzziness, intermediate signals should be fuzzy. Transferring fuzzy signal is computationally demanding. Special form of hierarchical fuzzy system is proposed to reduce computational burden.

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References

  1. Chung, F.-L., Duan, J.-C., Deriving Multistage, F.N.N.: Models From Takagi and Sugeno’s Fuzzy Systems. In: Proceedings of 1998 IEEE World Congress on Computational Intelligence, FUZZ-IEEE, pp. 1259–1264 (1998)

    Google Scholar 

  2. Chung, F.-L., Duan, J.-C.: On Multistage Fuzzy Neural Network Modeling. IEEE Transactions On Fuzzy Systems 8(2) (April 2000)

    Google Scholar 

  3. Duan, J.-C., Chung, F.-L.: A Mamdani Type Multistage Fuzzy Neural Network Model. In: Proceedings of 1998 IEEE World Congress on Computational Intelligence, FUZZ-IEEE, pp. 1253–1258 (1998)

    Google Scholar 

  4. Fukuda, T., Hasegawa, Y., Shimojima, K.: Structure Organization of Hierarchical Fuzzy Model using Genetic Algorithm. Japanese Journal of Fuzzy Theory and Systems 7(5), 631–643

    Google Scholar 

  5. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice Hall, Englewood Cliffs (1991)

    Google Scholar 

  6. Nowicki, R., Scherer, R.: A Hierarchical Fuzzy System With Fuzzy Intermediate Variables. In: Proceedings of The 9th Zittau Fuzzy Colloquium, Germany, pp. 88–93 (2001)

    Google Scholar 

  7. Nowicki, R., Scherer, R., Rutkowski, L.: A Hierarchical Neuro-Fuzzy System Based on S-Implications. In: 2003 International Joint Conference on Neural Networks, Portland, Oregon, USA (CD-ROM), June 17-27 (2003)

    Google Scholar 

  8. Paul, S., Kumar, S.: Subsethood-product fuzzy neural inference system (SuPFuNIS). IEEE Transactions on Neural Networks 13(3), 578–599 (2002)

    Article  Google Scholar 

  9. Raju, G.V.S., Zhou, J., Kisner, R.A.: Hierarchical fuzzy control. In: Advances in Intelligent Control, pp. 243–258. Taylor & Francis Ltd., Abington (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Kidney Disease Program, School of Medicine, University of Louisville, Louisville, KY, 40202, USA

    Adam E. Gaweda

  2. Dept. of Computer Engineering, Czestochowa Univ. of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Rafał Scherer

  3. Department of Artificial Intelligence, WSHE University in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

Authors
  1. Adam E. Gaweda
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  2. Rafał Scherer
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Editor information

Editors and Affiliations

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

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  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

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© 2004 Springer-Verlag Berlin Heidelberg

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Gaweda, A.E., Scherer, R. (2004). Fuzzy Number-Based Hierarchical Fuzzy System. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_42

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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