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Particle Swarm Optimization with Fuzzy Dynamic Parameters Adaptation for Modular Granular Neural Networks

  • Daniela Sánchez
  • Patricia MelinEmail author
  • Oscar Castillo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)

Abstract

In this paper a new method for Modular Granular Neural Network (MGNN) optimization with a granular approach is presented. A Particle Swarm Optimization technique is proposed to perform the granulation of information with a fuzzy dynamic parameters adaptation to prevent stagnation. The proposed fuzzy inference system seeks to adjust some PSO parameters such as w, C1 and C2 to ensure that the parameters have adequate values depending on the current behavior of the particles. The objective of the proposed PSO is design optimal MGNN architectures. The modular granular neural networks are applied to human recognition based on iris biometrics, where a benchmark database is used and the objective function in this work is the minimization of the error of recognition.

References

  1. 1.
    Auda, G., Kamel, M.: Modular neural networks: a survey. Int. J. Neural Syst. 9(2), 129–151 (1999)CrossRefGoogle Scholar
  2. 2.
    Bargiela, A., Pedrycz, W.: The roots of granular computing. In: IEEE International Conference on Granular Computing (GrC), pp. 806–809 (2006)Google Scholar
  3. 3.
    Castillo, O., Melin, P.: Soft Computing for Control of Non-Linear Dynamical Systems. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp. Accessed 12 Nov 2015
  5. 5.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  6. 6.
    Geem, Z.W., Yang, X.S., Tseng, C.L.: Harmony search and nature-inspired algorithms for engineering optimization. J. Appl. Math. 2013, 438158:1–438158:2 (2013)Google Scholar
  7. 7.
    Hassoun, M.: Fundamentals of Artificial Neural Networks. A Bradford Book, Cambridge (2003)zbMATHGoogle Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  9. 9.
    Jamal, A.: Granular computing. Int. J. Res. Cloud Eng. 2(3), 29–40 (2015)Google Scholar
  10. 10.
    Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, New Jersey (1997)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neuronal Networks. IEEE Press, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Khan, A., Bandopadhyaya, T., Sharma, S.: classification of stocks using self organizing map. Int. J. Soft Comput. Appl. 4, 19–24 (2009)Google Scholar
  13. 13.
    Lucic, P., Teodorovic, D.: Bee system: modeling combinatorial optimization transportation engineering problems by swarm intelligence. In: Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis, pp. 441–445 (2001)Google Scholar
  14. 14.
    Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn., pp. 119–122. Springer (2005)Google Scholar
  15. 15.
    Okamura, M., Kikuchi, H., Yager, R., Nakanishi, S.: Character diagnosis of fuzzy systems by genetic algorithm and fuzzy inference. In: Proceedings of the Vietnam-Japan Bilateral Symposium on Fuzzy Systems and Applications, Halong Bay, Vietnam, pp. 468–473 (1998)Google Scholar
  16. 16.
    Qian, Y., Zhang, H., Li, F., Hu, Q., Liang, J.: Set-based granular computing: A lattice model. Int. J. Approx. Reasoning 55, 834–852 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. J. 11, 5508–5518 (2011)CrossRefGoogle Scholar
  18. 18.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Rini, D.P., Shamsuddin, S.M., Yuhaniz, S.S.: Particle swarm optimization: technique, system and challenges. Int. J. Comput. Appl. 14(1), 19–27 (2011)Google Scholar
  20. 20.
    Sánchez, D., Melin, P.: Hierarchical genetic algorithms for type-2 fuzzy system optimization applied to pattern recognition and fuzzy control. In: Recent Advances on Hybrid Approaches for Designing Intelligent Systems, pp. 19–35 (2014)Google Scholar
  21. 21.
    Saravanan, K., Sasithra, S.: Review on classification based on artificial neural networks. Int. J. Ambient Syst. Appl. (IJASA) 2(4), 11–18 (2014)Google Scholar
  22. 22.
    Witten, I., Frank, E., Hall, E.: Fuzzy Logic for the Management of Uncertainty. Morgan Kaufmann, San Mateo (2011)Google Scholar
  23. 23.
    Yao, Y.Y.: On modeling data mining with granular computing. In: 25th International Computer Software and Applications Conference (COMPSAC), pp. 638–649 (2001)Google Scholar
  24. 24.
    Yao, Y.: Perspectives of granular computing. In: IEEE International Conference on Granular Computing (GrC), pp. 85–90 (2005)Google Scholar
  25. 25.
    Zadeh, L., Kacprzyk, J.: Fuzzy Logic for the Management of Uncertainty. Wiley-Interscience, New York (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Daniela Sánchez
    • 1
  • Patricia Melin
    • 1
    Email author
  • Oscar Castillo
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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