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A Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks: The Case of Time Series Prediction

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Soft Computing for Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 154))

Abstract

In this work, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an ”Interval Type-2 Fuzzy Neuron”, which is based on biological neural morphologies, followed by the learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture for the Takagi-Sugeno-Kang reasoning.

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References

  1. Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc., Upper Saddle River (1996)

    Google Scholar 

  2. Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, NJ (2001)

    MATH  Google Scholar 

  3. Pedrycz, W.: Neurocomputations in relational systems. IEEE Trans. on Pattern Anal. Mach. Intell. PAMI 13(3), 289–297 (1991)

    Article  Google Scholar 

  4. Pedrycz, W., Rocha, A.F.: Fuzzy-set based model of neurons and knowledge-based networks. IEEE Transactions on Fuzzy Systems 1, 254–266 (1993)

    Article  Google Scholar 

  5. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  6. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics 3(1), 28–44 (1973)

    MATH  MathSciNet  Google Scholar 

  7. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, Parts 1, 2, and 3. Information Sciences, 8, 9, 199–249, 301–357, 43–80 (1975)

    Google Scholar 

  8. Zadeh, L.A.: Fuzzy Logic. Computer 1(4), 83–93 (1988)

    Article  MathSciNet  Google Scholar 

  9. Zadeh, L.A.: Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering 1, 89–100 (1989)

    Article  Google Scholar 

  10. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 2, 103–111 (1996)

    Article  MathSciNet  Google Scholar 

  11. Karnik, N.N., Mendel, J.M.: An Introduction to Type-2 Fuzzy Logic Systems, Univ. of Southern Calif., Los Angeles, CA., (June 1998b)

    Google Scholar 

  12. Liang, Q., Mendel, J.: Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions Fuzzy Systems 8, 535–550 (2000)

    Article  Google Scholar 

  13. Mizumoto, M., Tanaka, K.: Some Properties of Fuzzy Sets of Type-2. Information and Control 31, 312–340 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  14. Castro, J.R.: Hybrid Intelligent Architecture Development for Time Series Forecasting. Masters Degree Thesis. Tijuana Institute of Technology (December 2005)

    Google Scholar 

  15. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems. In: Sugeno, M. (ed.) Industrial applications of fuzzy control, vol. 28, pp. 15–33. Elsevier Science Pub. Co, Amsterdam (1985)

    Google Scholar 

  16. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15, 116–132 (1985)

    MATH  Google Scholar 

  17. Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197, 287–289 (1977)

    Article  Google Scholar 

  18. Lapedes, S., Farber, R.: Nonlinear signal processing using neural networks: prediction and system modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (1987)

    Google Scholar 

  19. Crowder, R.S.: Predicting the Mackey-Glass time series with cascade-correlation learning. In: Touretzky, D., Hinton, G., Sejnowski, T. (eds.) Proc. of the 1990 Connectionist Models Summer School, Carnegic Mellon University, pp. 117–123 (1990)

    Google Scholar 

  20. Abraham, A.: Intelligent Systems: Architectures and Perspectives. In: Abraham, A., Jain, L., Kacprzyk, J. (eds.) Recent Advances in Intelligent Paradigms and Applications. Studies in Fuzziness and Soft Computing, ch. 1, pp. 1–35. Springer, Germany (2002)

    Google Scholar 

  21. Abraham, A., Khan, M.R.: Neuro-Fuzzy Paradigms for Intelligent Energy Management. In: Abraham, A., Jain, L., Jan van der Zwaag, B. (eds.) Innovations in Intelligent Systems: Design, Management and Applications. Studies in Fuzziness and Soft Computing, ch. 12, pp. 285–314. Springer, Germany (2003)

    Google Scholar 

  22. Nauck, D., Kruse, R.: NEFCLASS: A Neuro-Fuzzy Approach for the Classification of Data. In: George, K., et al. (eds.) Proceedings of ACM Symposium on Applied Computing, pp. 461–465. ACM Press, Nashville (1995)

    Chapter  Google Scholar 

  23. Nauck, D., Kruse, R.: A Neuro-Fuzzy Method to Learn Fuzzy Classification Rules from Data. Fuzzy Sets and Systems 89, 277–288 (1997)

    Article  MathSciNet  Google Scholar 

  24. Nauck D, D., Kruse, R.: Neuro-Fuzzy Systems for Function Approximation. Fuzzy Sets and Systems 101, 261–271 (1999)

    Article  MathSciNet  Google Scholar 

  25. Yager, R.R., Filev, D.P.: Adaptive Defuzzication for Fuzzy System Modeling. In: Proceedings of the Workshop of the North American Fuzzy Information Processing Society, pp. 135-142 (1992)

    Google Scholar 

  26. Castro, J.R., Castillo, O., Melin, P.: Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox. In: Proceedings of IFSA 2007, vol. 41, Part I, pp. 53–62 (2007)

    Google Scholar 

  27. Werbos, P.: Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University (1974)

    Google Scholar 

  28. Narendra, K.S., Parthsarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)

    Article  Google Scholar 

  29. Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing, Boston (1996)

    Google Scholar 

  30. Sepúlveda, R., Castillo, O., Melin, P., Díaz, A.R., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf. Sci. 177(10), 2023–2048 (2007)

    Article  Google Scholar 

  31. Sepulveda, R., Castillo, O., Melin, P., Montiel, O.: An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications. In: Melin, P., et al. (eds.) Analysis and Design of Intelligent Systems using Soft Computing Techniques, 1st edn. Studies in Fuzziness and Soft Computing, vol. 5, pp. 45–52. Springer, Germany (2007)

    Google Scholar 

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Authors and Affiliations

  1. Baja California Autonomous University, UABC, Tijuana, Mexico

    Juan R. Castro & Antonio Rodríguez-Díaz

  2. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo & Patricia Melin

Authors
  1. Juan R. Castro
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  2. Oscar Castillo
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  3. Patricia Melin
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  4. Antonio Rodríguez-Díaz
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Editor information

Oscar Castillo Patricia Melin Janusz Kacprzyk Witold Pedrycz

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Castro, J.R., Castillo, O., Melin, P., Rodríguez-Díaz, A. (2008). A Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks: The Case of Time Series Prediction. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70812-4_22

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  • DOI: https://doi.org/10.1007/978-3-540-70812-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70811-7

  • Online ISBN: 978-3-540-70812-4

  • eBook Packages: EngineeringEngineering (R0)

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