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Particle Swarm Optimization of the Fuzzy Integrators for Time Series Prediction Using Ensemble of IT2FNN Architectures

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Nature-Inspired Design of Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

This paper describes the construction of intelligent hybrid architectures and the optimization of the fuzzy integrators for time series prediction; interval type-2 fuzzy neural networks (IT2FNN). IT2FNN used hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). The IT2FNN is represented by Takagi–Sugeno–Kang reasoning. Therefore this TSK IT2FNN is represented as an adaptive neural network with hybrid learning in order to automatically generate an interval type-2 fuzzy logic system (TSK IT2FLS). We use interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Particle Swarm Optimization (PSO) was used for the optimization of membership functions (MFs) parameters of the fuzzy integrators. The Mackey-Glass time series is used to test of performance of the proposed architecture. Simulation results show the effectiveness of the proposed approach.

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References

  1. Ascia, G., Catania, V., Panno, D.: An Integrated Fuzzy-GA Approach for Buffer Management. IEEE Trans. Fuzzy Syst. 14(4), pp. 528–541. (2006).

    Google Scholar 

  2. Bonissone, P.P., Subbu, R., Eklund, N., Kiehl, T.R.: Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation. IEEE Trans. Evol Comput. 10(3), pp. 256–280. (2006).

    Google Scholar 

  3. Brocklebank J. C., Dickey, D.A.: SAS for Forecasting Series. SAS Institute Inc. Cary, NC, USA, pp. 6-140. (2003).

    Google Scholar 

  4. Brockwell, P. D., Richard, A.D.: Introduction to Time Series and Forecasting. Springer-Verlag New York, pp 1-219. (2002).

    Google Scholar 

  5. Castillo, O., Melin, P.: Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review, Information Sciences, Volume 205, pp. 1-19. (2012).

    Google Scholar 

  6. Castro J.R., Castillo O., Melin P., Rodriguez A.: A Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks: The Case of Time Series Prediction. Springer-Verlag Berlin Heidelberg, Vol. 15a, pp. 363-386. (2008).

    Google Scholar 

  7. Castro, J.R., Castillo, O., Martínez, L.G.: Interval type-2 fuzzy logic toolbox. Engineering Letters, 15(1), pp. 89–98. (2007).

    Google Scholar 

  8. Chiou, Y.-C., Lan, L.W.: Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method. Fuzzy Sets Syst. 152(3), pp. 617–635. (2005).

    Google Scholar 

  9. Deb, K.: A population-based algorithm-generator for real-parameter optimization. Springer, Heidelberg. (2005).

    Google Scholar 

  10. Engelbrecht, A.P.: Fundamentals of computational swarm intelligence. John Wiley & Sons, Ltd., Chichester. (2005).

    Google Scholar 

  11. Gaxiola, F., Melin, P., Valdez, F., Castillo, O.: Optimization of type-2 fuzzy weight for neural network using genetic algorithm and particle swarm optimization. Nature and Biologically Inspired Computing (NaBIC). World Congress on, vol., no., pp. 22-28. (2013).

    Google Scholar 

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

    Google Scholar 

  13. Hagras, H.: Comments on Dynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN). IEEE Transactions on Systems Man And Cybernetics Part B 36(5), pp. 1206–1209. (2006).

    Google Scholar 

  14. Haykin, S.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs. (2002) ISBN 0-13-048434-2.

    Google Scholar 

  15. Horikowa, S., Furuhashi, T., Uchikawa, Y.: On fuzzy modeling using fuzzy neural networks with the backpropagation algorithm. IEEE Transactions on Neural Networks 3, (1992).

    Google Scholar 

  16. Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Syst. 3, pp. 260–270. (1995).

    Google Scholar 

  17. Jang J.S.R.: Fuzzy modeling using generalized neural networks and Kalman fliter algorithm. Proc. of the Ninth National Conference on Artificial Intelligence. (AAAI-91), pp. 762-767. (1991).

    Google Scholar 

  18. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and Soft Computing. Prentice-Hall, New York. (1997).

    Google Scholar 

  19. Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. on Systems, Man and Cybernetics. Vol. 23, pp. 665-685 (1992).

    Google Scholar 

  20. Karnik, N.N., Mendel, J.M., Qilian L.: Type-2 fuzzy logic systems. Fuzzy Systems, IEEE Transactions on. vol.7, no.6, pp. 643,658. (1999).

    Google Scholar 

  21. Karnik, N.N., Mendel, J.M.: Applications of type-2 fuzzy logic systems to forecasting of time-series. Inform. Sci. 120, pp. 89–111. (1999).

    Google Scholar 

  22. Kennedy, J., Eberhart, R.: Particle swarm optimization. Neural Networks. Proceedings., IEEE International Conference on. vol. 4. pp. 1942-1948. (1995).

    Google Scholar 

  23. Lee, C.H., Hong, J.L., Lin, Y.C., Lai, W.Y.: Type-2 Fuzzy Neural Network Systems and Learning. International Journal of Computational Cognition 1(4), pp. 79–90. (2003).

    Google Scholar 

  24. Lee, C.-H., Lin, Y.-C.: Type-2 Fuzzy Neuro System Via Input-to-State-Stability Approach. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 317–327. Springer, Heidelberg (2007).

    Google Scholar 

  25. Lin, Y.-C., Lee, C.-H.: System Identification and Adaptive Filter Using a Novel Fuzzy Neuro System. International Journal of Computational Cognition 5(1) (2007).

    Google Scholar 

  26. Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science, Vol. 197, pp. 287-289. (1997).

    Google Scholar 

  27. Mackey, M.C.: Mackey-Glass. McGill University, Canada, http://www.sholarpedia.org/-article/Mackey-Glass_equation, September 5th, (2009).

  28. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, pp. 1–13. (1975).

    Google Scholar 

  29. Melin, P., Soto, J., Castillo, O., Soria, J.: A New Approach for Time Series Prediction Using Ensembles of ANFIS Models. Experts Systems with Applications. Elsevier, Vol. 39, Issue 3, pp 3494-3506. (2012).

    Google Scholar 

  30. Mendel, J.M.: Uncertain rule-based fuzzy logic systems: Introduction and new directions. Ed. USA: Prentice Hall, pp 25-200. (2000).

    Google Scholar 

  31. Mendel, J.M.: Why we need type-2 fuzzy logic systems. Article is provided courtesy of Prentice Hall, By Jerry Mendel. (2001).

    Google Scholar 

  32. Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization Intelligence: Advances and Applications. Information Science Reference. USA. pp. 18-40. (2010).

    Google Scholar 

  33. Pedrycz, W.: Fuzzy Evolutionary Computation. Kluwer Academic Publishers, Dordrecht. (1997).

    Google Scholar 

  34. Pedrycz, W.: Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Press, Dordrecht. (1996).

    Google Scholar 

  35. Pulido M., Melin P., Castillo O.: Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange. Information Sciences, Volume 280,, pp. 188-204. (2014).

    Google Scholar 

  36. Pulido, M., Mancilla, A., Melin, P.: An Ensemble Neural Network Architecture with Fuzzy Response Integration for Complex Time Series Prediction. Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, pp. 85-110. (2009).

    Google Scholar 

  37. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, NJ. (2003).

    Google Scholar 

  38. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE congress on evolutionary computation, pp. 69-73. (1998).

    Google Scholar 

  39. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp. 1945-1950. (1999).

    Google Scholar 

  40. Sollich, P., Krogh, A.: Learning with ensembles: how over-fitting can be useful. in: D.S. Touretzky M.C. Mozer, M.E. Hasselmo (Eds.). Advances in Neural Information Processing Systems 8, Denver, CO, MIT Press, Cambridge, MA, pp. 190-196. (1996).

    Google Scholar 

  41. Soto, J., Melin, P., Castillo, O.: Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators. International Journal Hybrid Intelligent Systems Vol. 11(3): pp. 211-226. (2014).

    Google Scholar 

  42. Takagi T., Sugeno M.: Derivation of fuzzy control rules from human operation control actions.Proc. of the IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55-60. (1983).

    Google Scholar 

  43. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybern. 15, pp. 116–132. (1985).

    Google Scholar 

  44. Wang, C.H., Cheng, C.S., Lee, T.-T.: Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN). IEEE Trans. on Systems, Man, and Cybernetics Part B: Cybernetics 34(3), pp. 1462–1477. (2004).

    Google Scholar 

  45. Wang, C.H., Liu, H.L., Lin, C.T.: Dynamic optimal Learning rate of A Certain Class of Fuzzy Neural Networks and Its Applications with Genetic Algorithm. IEEE Trans. Syst. Man, Cybern. 31(3), pp. 467–475. (2001).

    Google Scholar 

  46. Wu, D., Mendel, J.M.: A Vector Similarity Measure for Interval Type-2 Fuzzy Sets and Type-1 Fuzzy Sets. Information Sciences 178, pp. 381–402. (2008).

    Google Scholar 

  47. Wu, D., Wan Tan, W.: Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence 19(8), pp. 829–841. (2006).

    Google Scholar 

  48. Xiaoyu L., Bing W., Simon Y.: Time Series Prediction Based on Fuzzy Principles. Department of Electrical & Computer Engineering FAMU-FSU College of Engineering, Florida State University Tallahassee, FL 32310, (2002).

    Google Scholar 

  49. Zadeh L. A.: Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems, 4(2), 103, (1996).

    Google Scholar 

  50. Zadeh L. A.: Fuzzy Logic. Computer, Vol. 1, No. 4, pp. 83-93. (1988).

    Google Scholar 

  51. Zadeh, L.A.: Fuzzy Logic, Neural Networks and Soft Computing. Communications of the ACM 37(3), pp. 77–84. (1994).

    Google Scholar 

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Jesus Soto, Patricia Melin & Oscar Castillo

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  1. Jesus Soto
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  2. Patricia Melin
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  3. Oscar Castillo
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Correspondence to Patricia Melin .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Soto, J., Melin, P., Castillo, O. (2017). Particle Swarm Optimization of the Fuzzy Integrators for Time Series Prediction Using Ensemble of IT2FNN Architectures. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_9

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