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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2013: Artificial Intelligence and Soft Computing pp 294–305Cite as

Evolutionary Strategy for the Fuzzy Flip-Flop Neural Networks Supervised Learning Procedure

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

Abstract

The aim of this paper is present the usage of (μ + λ) Evolutionary Strategy to evolve the architecture, and primarily the connection weights, for Fuzzy Flip-Flop Neural Networks. Due to the specific transfer function of this fuzzy-based neural network and its numerical derivatives, Back Propagation algorithm can be used for the training process, but it has very week convergence rates. Therefore Evolutionary Strategy as a heuristic learning algorithm will be applied here. In the article some numerical properties of proposed approach will be exposed. They will concern on natural example such as function approximation and data classification. It exhibits better results in terms of faster convergence and least square-error. Finally some conclusions and ideas for future work will be under discussion.

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References

  1. Angulo, C., Parra, X., Catala, A.: K-SVCR. A Support Vector Machine for Multi-class Classification. Neurocomputing 55, 57–77 (2003)

    Article  Google Scholar 

  2. Arabas, J.: Evolutionary Computation for Global Optimization – Current Trends. Journal of Telecommunications and Information Technology 4, 5–10 (2011)

    Google Scholar 

  3. Bergh, V.F., Engelbrecht, A.: Cooperative Learning in Neural Networks Using Particle Swarm Optimizers. South African Computer Journal 26, 84–90 (2000)

    Google Scholar 

  4. Brownlee, J.: Clever Algorithms: Nature-Inspired Programming Recipes, Lulu Enterprises (2011)

    Google Scholar 

  5. Gal, L., Botzheim, J., Koczy, L.T.: Improvements to the Bacterial Memetic Algorithm used for Fuzzy Rule Base Extraction. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Istanbul, pp. 38–43 (2008)

    Google Scholar 

  6. Gal, L., Botzheim, J., Koczy, L.T.: Function Approximation Performance of Fuzzy Neural Networks Based on Frequently Used Fuzzy Operations and a Pair of New Trigonometric Norms. In: IEEE International Conference on Fuzzy Systems, Barcelona, pp. 1–8 (2010)

    Google Scholar 

  7. Diamond, J., Pedrycz, W., McLeod, D.: Fuzzy JK Flip-Flops as Computational Structures: Design and Implementation. IEEE Transactions on Circuits and Systems II, Analog and Digital Signal Processing 41(3), 215–226 (1994)

    Article  Google Scholar 

  8. Gniewek, L., Kluska, J.: Family of Fuzzy JK Flip-Flops Based on Bounded Product, Bounded Sum and Complementation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28(6), 861–868 (1998)

    Article  Google Scholar 

  9. Hirota, K., Ozawa, K.: The Concept of Fuzzy Flip-Flop. Man and Cybernetics 19(5), 980–997 (1989)

    Article  MathSciNet  Google Scholar 

  10. Hirota, K., Pedrycz, W.: Neurocomputations with Fuzzy Flip-Flops. In: Proceedings of International Joint Conference on Neural Networks, Nagoya, vol. 2, pp. 1867–1870 (1993)

    Google Scholar 

  11. Jones, A.J.: Genetic Algorithms and Their Applications to the Design of Neural Networks. Neural Computing and Applications, 32–45 (1993)

    Google Scholar 

  12. De Jong, K.A.: Evolutionary Computation: A Unified Approach. MIT Press, Cambridge (2006)

    Google Scholar 

  13. Kacprzyk, J.: Fuzzy Sets in Systems Analysis. PWN, Warsaw (1986)

    Google Scholar 

  14. Kowalski, P.A., Kulczycki, P.: Data Sample Reduction for Classification of Interval Information using Neural Network Sensitivity Analysis. In: Dicheva, D., Dochev, D. (eds.) AIMSA 2010. LNCS, vol. 6304, pp. 271–272. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Kowalski, P.A., Lukasik, S., Charytanowicz, M., Kulczycki, P.: Data-Driven Fuzzy Modelling and Control with Kernel Density Based Clustering Technique. Polish Journal of Environmental Studies 17, 83–87 (2008)

    Google Scholar 

  16. Koczy, L.T., Lovassy, R.: Fuzzy Flip-Flops and Neural Nets? In: IEEE International Fuzzy Systems Conference, London, pp. 1–6 (2007)

    Google Scholar 

  17. Kulczycki, P.: Statistical Inference for Fault Detection: A Complete Algorithm Based on Kernel Estimators. Kybernetika 38(2), 141–168 (2002)

    MathSciNet  MATH  Google Scholar 

  18. Kulczycki, P., Kowalski, P.A.: Bayes Classification of Imprecise Information of Interval Type. Control and Cybernetics 40(1), 101–123 (2011)

    MathSciNet  Google Scholar 

  19. Kulczycki, P., Hryniewicz, O., Kacprzyk, J. (eds.): Information Technologies for Systems Research. WNT, Warsaw (2007) (in Polish)

    Google Scholar 

  20. Kulczycki, P., Charytanowicz, M., Kowalski, P.A., Lukasik, S.: The Complete Gradient Clustering Algorithm: Properties in Practical Applications. Journal of Applied Statistics 39(6), 1211–1224 (2012)

    Article  MathSciNet  Google Scholar 

  21. Lovassy, R., Koczy, L.T., Gal, L.: Multilayer Perception Implemented by Fuzzy Flip-Flops. In: IEEE World Congress on Computational Intelligence, Hong Kong, pp. 1683–1688 (2008)

    Google Scholar 

  22. Lovassy, R., Koczy, L.T., Gal, L.: Optimizing Fuzzy Flip-Flop Based Neural Networks by Bacterial Memetic Algorithm. In: IFSA/EUSFLAT, Lisbon, pp. 1508–1513 (2009)

    Google Scholar 

  23. Lukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Fuzzy Model Identification Using Kernel-Density-Based Clustering. In: Atanassov, K., Chountas, P., Kacprzyk, J., Krawczak, M.P., Melo-Pinto, P., Szmidt, E., Zadrozny, S. (eds.) Developments in Fuzzy Sets, Intuitionistic Fuzzy Nets and Related Topics. Applications, vol. II, pp. 135–146. EXIT, Warszawa (2008)

    Google Scholar 

  24. Ozawa, K., Hirota, K., Koczy, L.T.: Fuzzy flip-flop. In: Patyra, M.J., Mlynek, D.M. (eds.) Fuzzy Logic Implementation and Applications, pp. 97–236. Wiley (1996)

    Google Scholar 

  25. Mandziuk, J.: Solving the N-Queens Problem with a Binary Hopfield-Type Network. Biological Cybernetics 72(5), 439–446 (1995)

    Article  MATH  Google Scholar 

  26. Marquardt, D.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of Applied Mathematics 11, 431–441 (1963)

    MathSciNet  MATH  Google Scholar 

  27. Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)

    Book  Google Scholar 

  28. Whitley, D., Starkweather, T., Bogart, C.: Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity. Technical Report Department of Computer Science, Colorado State University, pp. 89–117 (1989)

    Google Scholar 

  29. Iris Data Set, http://archive.ics.uci.edu/ml/datasets/Iris

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Author information

Authors and Affiliations

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

    Piotr A. Kowalski

  2. Department of Automatic Control and Information Technology, Cracow University of Technology, ul. Warszawska 24, PL-31-155, Cracow, Poland

    Piotr A. Kowalski

Authors
  1. Piotr A. Kowalski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Kowalski, P.A. (2013). Evolutionary Strategy for the Fuzzy Flip-Flop Neural Networks Supervised Learning Procedure. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_27

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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