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Fuzzy logic research work in Mexico motivated by Lotfi Zadeh

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Title of paper: Fuzzy logic research work in Mexico motivated by Lotfi Zadeh
Author(s):
Oscar Castillo
Tijuana Institute of Technology, Tijuana, Mexico
Patricia Melin
Tijuana Institute of Technology, Tijuana, Mexico
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 27 (2021), Number 2, pages 1–10
DOI: https://doi.org/10.7546/nifs.2021.27.2.1-10
Download:  PDF (669  Kb, File info)
Abstract: We provide in this article a short review of the research work that has been done in Mexico on developing new methods and theory for designing intelligent systems utilizing type-2 fuzzy systems in combination with soft computing techniques. Soft Computing (SC) is an area formed by intelligent paradigms, like fuzzy systems, neural networks, and bio-inspired and swarm algorithms, which may be utilized to build high performance hybrid systems. The combination of type-2 fuzzy systems with SC enables the constructing of efficient intelligent systems for solving complex problems in a wide diversity of areas, such as control, pattern recognition, medical diagnosis and others. We also recall some of the main moments and memories of encounters and meetings with the father of fuzzy logic (Prof. L. Zadeh), which were very positive and motivated us to continue his work and legacy.
Keywords: Fuzzy logic, Type-2 fuzzy logic, Fuzzy systems.
References:
  1. Castillo, O., & Melin, P. (2001). Soft Computing for Control of Non-Linear Dynamical Systems. Springer-Verlag, Heidelberg, Germany.
  2. Castillo, O., & Melin, P. (2003). Soft Computing and Fractal Theory for Intelligent Manufacturing. Springer-Verlag, Heidelberg, Germany.
  3. Castillo, O., & Melin, P. (2008). Type-2 Fuzzy Logic: Theory and Applications. Springer-Verlag, Heidelberg, Germany.
  4. Castillo, O. (2012). Type-2 Fuzzy Logic in Intelligent Control Applications. Springer-Verlag, Heidelberg, Germany.
  5. Melin, P., & Castillo, O. (2002). Modelling, Simulation and Control of Non-Linear Dynamical Systems. Taylor and Francis, London, Great Britain.
  6. Melin, P., & Castillo, O. (2005). Hybrid Intelligent Systems for Pattern Recognition. Springer-Verlag, Heidelberg, Germany.
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  10. Castillo, O., & Amador-Angulo, L. (2018). A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Information Sciences 460–461, 476–496.
  11. Melin, P, Olivas, F, Castillo, O, Valdez, F, Soria, J, & Garcia, J. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Elsevier Exp Syst Appl, 40 (8): 3196–3206.
  12. Neyoy, H., Castillo, O., & Soria, J. (2012). Dynamic fuzzy logic parameter tuning for ACO and its application in TSP Problems. Studies in Computational Intelligence 451, Springer, 259–271.
  13. Olivas, F., Valdez, F., Castillo, O. & Melin, P. (2016). Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft Computing, 20(3), 1057–1070.
  14. Olivas, F., Valdez, F., Castillo, O. & Melin P. (2019). Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. Information Sciences 476, 159–175.
  15. Sanchez, D., Melin, P., Castillo, O. (2017). Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. of AI 64: 172–186.
  16. González, B., Valdez, F., Melin, P., & Prado-Arechiga, G. (2015). Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition. Expert Systems with Applications, 42 (14), 5839–5847.
  17. González, B., Valdez, F., Melin, P., & Prado-Arechiga, G. (2015). Fuzzy logic in the gravitational search algorithm enhanced using fuzzy logic with dynamic alpha parameter value adaptation for the optimization of modular neural networks in echocardiogram recognition. Applied Soft Computing 37, 245-–254.
  18. Miramontes, I., Guzman, J., Melin, P., & Prado-Arechiga, G. (2018). Optimal Design of Interval Type-2 Fuzzy Rate Level Classification Systems Using the Bird Swarm Algorithm. Algorithms, 11 (12), 206.
  19. Ontiveros, E., Melin, P., & Castillo, O. (2018). High order α-planes integration: A new approach to computational cost reduction of General Type-2 Fuzzy Systems. Eng. Appl. Artif. Intell., 74: 186–197.
  20. 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.
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