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Issue:Intuitionistic fuzzy logic adaptation of particle swarm optimization

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http://ifigenia.org/wiki/issue:nifs/23/2/95-102
Title of paper: Intuitionistic fuzzy logic adaptation of particle swarm optimization
Author(s):
Patricia Melin
Tijuana Institute of Technology,, Tijuana BC México
pmelin@tectijuana.mx
Daniela Sánchez
Tijuana Institute of Technology,, Tijuana BC México
danielasanchez.itt@hotmail.com
Pencho Marinov
Bulgarian Academy of Sciences, Sofia, Sofia, Bulgaria
pencho@parallel.bas.bg
Presented at: 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria
Published in: "Notes on IFS", Volume 23, 2017, Number 2, pages 95—102
Download:  PDF (157 Kb  Kb, File info)
Abstract: In this paper a new Modular Neural Network (MNN) optimization is proposed, where a particle swarm optimization with an intuitionistic fuzzy dynamic parameter adaptation designs optimal MNNs architectures. This design consists in to find the number of hidden layers for each sub module with their respective number of neurons, learning method, error goal and the percentage of data used for the training phase. The proposed intuitionistic fuzzy adaptation seeks to avoid stagnation of error of recognition during iterations updating some PSO parameters.
Keywords: Intuitionistic fuzzy logic, Particle Swarm Optimization, Iris recognition, Human recognition.
AMS Classification: 03E72
References:
  1. Abraham, A. (2005). Hybrid intelligent systems: evolving intelligence in hierarchical layers. Studies in Fuzziness and Soft Computing, 173, 159−179.
  2. Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11, 47−58.
  3. Atanassov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Set and Systems, 20(1), 87–96.
  4. Atanassov, K. (2016). Intuitionistic Fuzzy Sets. VII ITKR Session, Sofia, 20-23 June 1983, Reprinted: Int. J. Bioautomation, 20(S1), S1–S6.
  5. Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Springer, Heidelberg.
  6. Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory. Springer Physica-Verlag, Berlin.
  7. Atanassov, K., Vassilev, P., & Tsvetkov, R. (2013). Intuitionistic Fuzzy Sets, Measures and Integrals. Academic Publishing House "Prof. Marin Drinov".
  8. Atanassov, K. (2016). Review and new results on intuitionistic fuzzy sets. Mathematical Foundations of Artificial Intelligence Seminar, Sofia, 1988, Preprint IM-MFAIS-1-88. Reprinted: Int. J. Bioautomation, 20(S1), S7–S16.
  9. Castillo, O., Ramirez, E., & Roeva, O. (2017). Water cycle algorithm augmentation with fuzzy and intuitionistic fuzzy dynamic adaptation of parameters. Notes on Intuitionistic Fuzzy Sets, 23(1), 79–94.
  10. Castillo, O., Neyoy, H., Soria, J., Melin, P., & Valdez, F. (2015). A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Applied Soft Computing, 28, 150−159.
  11. Ch’ng, S. I., Seng, K. P., & Ang, L. (2012). Modular dynamic RBF neural network for face recognition. 2012 IEEE Conference on Open Systems, doi: 10.1109/ICOS.2012.6417629.
  12. Choubey, D. K., & Pau, S. (2016). GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. International Journal of Intelligent Systems and Applications, 1, 49−59.
  13. Farooq, M. (2015). Genetic algorithm technique. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1891−1898.
  14. Hicham, A., Mohammed, B., & Anas, S. (2012). Hybrid intelligent system for sale forecasting using Delphi and adaptive fuzzy back-propagation neural networks. International Journal of Advanced Computer Science and Applications, 3(11), 122−130.
  15. Jain, A. K., Nandakumarb, K., & Ross, A. (2016). 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognition Letters, 79, 80−105.
  16. Jang, J., Sun, C., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. New Jersey: Prentice Hall.
  17. Kareem, M. H., Jassim, J. M., & Al-Hareeb, N. K. (2016). Mathematical modelling of particle swarm optimization algorithm. International Journal of Advanced Multidisciplinary Research, 3(4), 54−59.
  18. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of the IEEE international Joint Conference on Neuronal Network, 1942–1948.
  19. Landassuri-Moreno, V. M., & Bullinaria, J. A. (2011). Biasing the evolution of modular neural networks. IEEE Congress on Evolutionary Computation, doi: 10.1109/CEC.2011. 5949855.
  20. Maia, D., & Trindade, R. (2016). Face detection and recognition in color images under Matlab. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(2), 13−24.
  21. Melin, P., & Castillo, O. (2005). Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, Springer, 119−122.
  22. Mustafa, D., Osman, U., & Tayyab, W. (2014). Washing machine using fuzzy logic. Automation, Control and Intelligent Systems, 2(3), 27−32.
  23. Perez, J., Valdez, F., Roeva, O., & Castillo, O. (2016). Parameter adaptation of the Bat Algorithm, using Type-1, interval Type-2 fuzzy logic and intuitionistic fuzzy logic. Notes on Intuitionistic Fuzzy Sets, 22(2), 87–98.
  24. Rini, D. P., Shamsuddin, S. M., & Yuhaniz, S. S. (2011). Particle swarm optimization: technique, system and challenges. International Journal of Computer Applications, 14(1), 19−27.
  25. Roeva, O., & Michalíková, A. (2013). Generalized net model of intuitionistic fuzzy logic control of genetic algorithm parameters. Notes on Intuitionistic Fuzzy Sets, 19(2), 71–76.
  26. Roeva, O., & Michalíková, A. (2014). Intuitionistic fuzzy logic control of metaheuristic algorithms' parameters via a Generalized net. Notes on Intuitionistic Fuzzy Sets, 20(4), 53–58.
  27. Roeva, O., Perez, J., Valdez, F., & Castillo, O. (2016). Intercriteria analysis of bat algorithm with parameter adaptation using Type-1 and interval Type-2 fuzzy systems. Notes on Intuitionistic Fuzzy Sets, 22(3), 91–105.
  28. Sánchez, D., Melin, P., & Castillo, O. (2015). Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Information Sciences, 309, 73−101.
  29. Sánchez, D., & Melin, P. (2014). Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Engineering Applications of Artificial Intelligence, 27, 41−56.
  30. Seera, M., & Lim, C. P. (2014). A hybrid intelligent system for medical data classification. Expert Systems with Applications, 41, 2239−2249.
  31. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929−1958.
  32. Zadeh, L. A. (1979). Fuzzy Sets and Information Granulation. North Holland Publishing.
  33. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
  34. Zurada, J. M. (1992). Introduction to Artificial Neural Systems. West Group.
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