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Issue:Genetic optimization of type-1, interval and intuitionistic fuzzy recognition systems

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http://ifigenia.org/wiki/issue:nifs/24/2/106-128
Title of paper: Genetic optimization of type-1, interval and intuitionistic fuzzy recognition systems
Author(s):
Patricia Melin
Tijuana Institute of Technology, Tijuana Mexico
pmelin@tectijuana.mx
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 2, pages 106-128
DOI: https://doi.org/10.7546/nifs.2018.24.2.106-128
Download:  PDF (174 Kb  Kb, Info)
Abstract: In this paper a new method for fuzzy system optimization is presented. The proposed method performs the intuitionistic or type-2 fuzzy inference system design using a hierarchical genetic algorithm as an optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed considering a human recognition application. Human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea isto perform the combination of responses in modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The results obtained show the effectiveness of the proposed method for designing optimal structures offuzzy systems. The design of optimal structures of fuzzy systems include among other parameters; type of fuzzy logic (Type-1, interval type-2 and intuitionistic fuzzy logic), type of model (Mamdani model or Sugeno model), and consequents of the fuzzy if-then rules.
Keywords: Modular neural networks, Type-1 fuzzy logic, Interval type-2 fuzzy logic, Intuitionistic fuzzy logic, Human recognition, Hierarchical genetic algorithm.
AMS Classification: 03E72
References:
  1. Atanassov, K. (1986). Intuitionistic fuzzy sets, Fuzzy Set and Systems, 20(1), 87–96.
  2. Bhatia, S., Bawa, A., & Kaur Attri, V. (2015). A review on genetic algorithm to deal with optimization of parameters of constructive cost model. International Journal of Advanced Research in Computer and Communication Engineering, 4(4), 405–408.
  3. Castillo, O.,& Melin P. (2008) Type-2 Fuzzy Logic Theory and Applications, Springer Verlag, Berlin, 20–43.
  4. 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.
  5. Castro, J. R., Castillo, O., Melin, P., & Rodríguez-Díaz, A. (2008). Building Fuzzy Inference Systems with a New Interval Type-2 Fuzzy Logic Toolbox", Transactions on Computational Science,1, 104–114.
  6. Database Ear Recognition Laboratory from the University of Science & Technology Beijing (USTB). Found on the Web page: http://www.ustb.edu.cn/resb/en/index.htm (Accessed 21 September 2009).
  7. Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). Found on the Web page: http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp (Accessed 21 September 2009).
  8. Fister, I., Rauter, S., Yang, X. S., & Ljubič, K. (2015). Planning the sports training sessions with the bat algorithm. Neurocomputing, 149, 993–1002.
  9. Garcia-Capulin, C. H., Cuevas, F. J., Trejo-Caballero, G., & Rostro-Gonzalez, H. (2015). A hierarchical genetic algorithm approach for curvefitting with B-splines. Genetic Programming and Evolvable Machines, 16(2), 151–166.
  10. Gaxiola, F., Melin, P., Valdez, F., & Castillo, O (2012) Neural Network with Type-2 Fuzzy Weights Adjustment for Pattern Recognition ofthe Human Iris Biometrics, MICAI, 2, 259–270.
  11. González, B., Valdez, F., Melin, P., & Prado-Arechiga, G. (2015). Fuzzy logic in the gravitational search algorithm for the optimizationof modular neural networks in pattern recognition. Expert Systems with Applications, 42(14), 5839–5847.
  12. Gutierrez, L., Melin P. & Lopez, M. (2010) Modular Neural Network for Human Recognition from Ear Images Using Wavelets, Soft Computing for Recognition Based on Biometrics, 121–135.
  13. Gutierrez, L., Melin P. & Lopez, M. (2010) Modular neural network integrator for human recognition from ear images, IJCNN, 1–5.
  14. Hidalgo, D., Melin, P., & Castillo, O. (2012). An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms. Expert Systems with Applications, 39(4), 4590–4598.
  15. Man, K. F., Tang, K. S., & Kwong, S. (1999). Genetic Algorithms: Concepts and Designs, Springer.
  16. Martínez-Soto, R., Castillo, O., & Aguilar, L. T. (2014). Type-1 and Type-2 fuzzy logic controller design using a Hybrid PSO–GA optimization method. Information Sciences, 285, 35–49.
  17. Masek, L.& Kovesi, P. (2003) MATLAB Source Code fora Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia.
  18. Melin P.(2015) Pattern Recognition with Modular Neural Networks and Type-2 Fuzzy Logic, Handbook of Computational Intelligence, 1509–1515.
  19. Melin, P., & Castillo, O. (2014). A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Applied soft computing, 21, 568–577.
  20. Melin, P., Gonzalez, C. I., Castro, J. R., Mendoza,O., & Castillo, O. (2014). Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Transactions on Fuzzy Systems, 22(6), 1515–1525.
  21. Melin, P., Sánchez, D., & Castillo, O. (2012). Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Information Sciences, 197, 1–19.
  22. Mendel, J. M. (2014). General type-2 fuzzy logic systems made simple: a tutorial. IEEE Transactions on Fuzzy Systems, 22(5), 1162–1182.
  23. Murmu, S., & Biswas, S. (2015). Application of fuzzy logic and neural network in crop classification: A review. Aquatic Procedia, 4, 1203–1210.
  24. Nandy, S., Yang, X. S., Sarkar, P. P., & Das, A. (2015). Color image segmentation by cuckoo search. Intelligent Automation & Soft Computing, 21(4), 673–685.
  25. Porwal, A., Carranza, E. J. M., & Hale, M. (2004). A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology, 36(7), 803–826.
  26. ProQuest Information and Learning Company., Historyof Voice Recognition Technology, The, Information Management Journal. 07 Oct, 2009.
  27. Saleh, M. (2006) Using Ears as a Biometric for Human Recognition, Arab Academy for Science and Technology and Maritime Transport, Cairo, Egypt, September 2006.
  28. 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.
  29. Sánchez D., & Melin P. (2016) Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation, Springer.
  30. Santos, J. M., Alexandre, L. A., & De Sa, J. M. (2006). Modular neural network task decomposition via entropic clustering, in: ISDA, 1,62–67.
  31. Seitzer, J. (2014) Zooming In, Zooming Out: A Framework for Hierarchical Genetic Algorithms, Systemics, Cybernetics And Informatics, 12(4), 1–5.
  32. Singh, Y. N. (2015). Human recognition using Fisher׳s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing, 167, 322–335.
  33. Tahmasebi, P., & Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & geosciences, 42, 18–27.
  34. Tahmasebi, P., & Hezarkhani, A. (2011). Applicationof a modular feedforward neural network for grade estimation. Natural resources research, 20(1), 25–32.
  35. Tang, K.S., Man, K.F., Kwong, S., Liu, Z.F.( 1998) Minimal fuzzy memberships and rule using hierarchical genetic algorithms, IEEE Trans. Ind. Electron.45 (1), 162–169.
  36. Vázquez J.C., Valdez F. & Melin P. (2015) Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions, Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, 163–178.
  37. Zadeh, L. A. (1965) Fuzzy Sets, Journal of Information and Control, 8, 338–353.
  38. Zadeh, L. A. (2008) Is there a need for fuzzy logic?, Information Sciences, 178, 2751–2779.
  39. Zadeh, L. A. (2005). Toward a generalized theory ofuncertainty (GTU)––an outline. Information sciences, 172(1-2), 1–40.
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