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Genetic Optimization of Type-1, Type-2 and Intuitionistic Fuzzy Recognition Systems

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Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives (IWIFSGN 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1081))

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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 is to 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 of fuzzy systems.

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References

  1. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Set Syst. 20(1), 87–96 (1986)

    Article  Google Scholar 

  2. Bhatia, S., Bawa, A., Attri, V.K.: A review on genetic algorithm to deal with optimization of parameters of constructive cost model. Int. J. Adv. Res. Comput. Commun. Eng. 4(4), 405–408 (2015)

    Google Scholar 

  3. Castillo, O., Melin, P.: Type-2 Fuzzy Logic Theory and Applications, pp. 20–43. Springer, Heidelberg (2008)

    Book  Google Scholar 

  4. Castillo, O., Neyoy, H., Soria, J., Melin, P., Valdez, F.: A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. 28, 150–159 (2015)

    Article  Google Scholar 

  5. Castro, J.R., Castillo, O., Melin, P., Rodriguez-Diaz, A.: Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox. In: Transactions on Computational Science, vol. 1, pp. 104–114 (2008)

    Google Scholar 

  6. Database Ear Recognition Laboratory from the University of Science & Technology Beijing (USTB). http://www.ustb.edu.cn/resb/en/index.htm. Accessed 21 Sept 2009

  7. Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp. Accessed 21 Sept 2009

  8. Fister, I., Rauter, S., Yang, X.S., Ljubic, K., Fister Jr., I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 14, 993–1002 (2015)

    Article  Google Scholar 

  9. Garcia-Capulin, C.H., Cuevas, F.J., Trejo-Caballero, G., Rostro-Gonzalez, H.: A hierarchical genetic algorithm approach for curve fitting with B-splines. Genet. Program Evolvable Mach. 16(2), 151–166 (2015)

    Article  Google Scholar 

  10. Gaxiola, F., Melin, P., Valdez, F., Castillo, O.: Neural network with type-2 fuzzy weights adjustment for pattern recognition of the human iris biometrics. In: MICAI, no. 2, pp. 259–270 (2012)

    Google Scholar 

  11. González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition. Expert Syst. Appl. 42(14), 5839–5847 (2015)

    Article  Google Scholar 

  12. Gutierrez, L., Melin, P., Lopez, M.: Modular neural network for human recognition from ear images using wavelets. In: Soft Computing for Recognition Based on Biometrics, pp. 121–135 (2010)

    Google Scholar 

  13. Gutierrez, L., Melin, P., Lopez, M.: Modular neural network integrator for human recognition from ear images. In: IJCNN, pp. 1–5 (2010)

    Google Scholar 

  14. Hidalgo, D., Melin, P., Castillo, O.: An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms. Expert Syst. Appl. 39(4), 4590–4598 (2012)

    Article  Google Scholar 

  15. Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer, London (1999)

    Book  Google Scholar 

  16. Martínez-Soto, R., Castillo, O., Aguilar, L.T.: Type-1 and type-2 fuzzy logic controller design using a Hybrid PSO-GA optimization method. Inf. Sci. 285, 35–49 (2014)

    Article  MathSciNet  Google Scholar 

  17. Masek, L., Kovesi, P.: MATLAB source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia (2003)

    Google Scholar 

  18. Melin, P.: Pattern recognition with modular neural networks and type-2 fuzzy logic. In: Handbook of Computational Intelligence, pp. 1509–1515 (2015)

    Google Scholar 

  19. Melin, P., Castillo, O.: A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl. Soft Comput. 21, 568–577 (2014)

    Article  Google Scholar 

  20. Melin, P., González, C., Castro, J.R., Mendoza, O., Castillo, O.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  21. Melin, P., Sánchez, D., Castillo, O.: Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)

    Article  Google Scholar 

  22. Mendel, J.M.: General type-2 fuzzy logic systems made simple: a tutorial. IEEE Trans. Fuzzy Syst. 22(5), 1162–1182 (2014)

    Article  Google Scholar 

  23. Murmu, S., Biswas, S.: Application of fuzzy logic and neural network in crop classification: a review. Aquat. Procedia 4, 1203–1210 (2015)

    Article  Google Scholar 

  24. Nandy, S., Yang, X.S., Sarkar, P.P., Das, A.: Color image segmentation by cuckoo search. Intell. Autom. Soft Comput. 21(4), 673–685 (2015)

    Article  Google Scholar 

  25. Porwal, A., Carranza, E.J.M., Hale, M.: A hybrid neuro-fuzzy model for mineral potential mapping. Math. Geol. 36(7), 803–826 (2004)

    Article  Google Scholar 

  26. ProQuest Information and Learning Company: History of voice recognition technology. Inf. Manage. J. (2009). http://findarticles.com/p/articles/mi_qa3937/is_200401/ai_n9383074/

  27. Saleh, M.: Using ears as a biometric for human recognition. Arab Academy for Science and Technology and Maritime Transport, Cairo, Egypt, September 2006

    Google Scholar 

  28. Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. AI 27, 41–56 (2014)

    Article  Google Scholar 

  29. Sánchez, D., Melin, P.: Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation, 1st edn. Springer, Cham (2016)

    Book  Google Scholar 

  30. Santos, J.M., Alexandre, L.A., Marques de Sa, J.: Modular neural network task decomposition via entropic clustering. In: ISDA, vol. 1, pp. 62–67 (2006)

    Google Scholar 

  31. Seitzer, J.: Zooming in, zooming out: a framework for hierarchical genetic algorithms. Syst. Cybern. Inform. 12(4), 1–5 (2014)

    Google Scholar 

  32. Singh, Y.N.: Human recognition using Fisher׳s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing 167, 322–335 (2015)

    Article  Google Scholar 

  33. Tahmasebi, P., Hezarkhani, A.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27 (2012)

    Article  Google Scholar 

  34. Tahmasebi, P., Hezarkhani, A.: Application of a modular feedforward neural network for grade estimation. Nat. Resour. Res. 20(1), 25–32 (2011)

    Article  Google Scholar 

  35. Tang, K.S., Man, K.F., Kwong, S., Liu, Z.F.: Minimal fuzzy memberships and rule using hierarchical genetic algorithms. IEEE Trans. Ind. Electron. 45(1), 162–169 (1998)

    Article  Google Scholar 

  36. Vázquez, J.C., Valdez,, F., Melin, P.: Comparative study of particle swarm optimization variants in complex mathematics functions. In: Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, pp. 163–178 (2015)

    Google Scholar 

  37. Zadeh, L.A.: Fuzzy sets. J. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  38. Zadeh, L.A.: Is there a need for fuzzy logic? Inf. Sci. 178, 2751–2779 (2008)

    Article  MathSciNet  Google Scholar 

  39. Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU)—an outline. Inf. Sci. 172, 1–40 (2005)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

Authors
  1. Patricia Melin
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Department of Bioinformatics and Mathematical Modelling, Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Sofia, Bulgaria

    Krassimir T. Atanassov

  2. Department of Bioinformatics and Mathematical Modelling, Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Sofia, Bulgaria

    Vassia Atanassova

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

    Janusz Kacprzyk

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

    Andrzej Kaluszko

  5. WIT- Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

  6. Polish Acadamy of Sciences, Systems Research Institute, Warsaw, Poland

    Jan W. Owsinski

  7. Intelligent Systems Laboratory, Faculty of Technical Sciences, Bourgas, Bulgaria

    Sotir Sotirov

  8. Intelligent Systems Laboratory, Prof. Assen Zlatarov University, Burgas, Bulgaria

    Evdokia Sotirova

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

    Eulalia Szmidt

  10. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Slawomir Zadrozny

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Melin, P. (2021). Genetic Optimization of Type-1, Type-2 and Intuitionistic Fuzzy Recognition Systems. In: Atanassov, K., et al. Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives. IWIFSGN 2018. Advances in Intelligent Systems and Computing, vol 1081. Springer, Cham. https://doi.org/10.1007/978-3-030-47024-1_28

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