Abstract
This paper presents the comparison of fuzzy controller optimization results using dynamic parameter adjustment Type-1 (T1) and Interval Type-2 (T2) fuzzy logic to the Firefly Algorithm (FA). The FA is used for optimizations parameters of the membership functions in the fuzzy controllers. The dynamic adjustment is applied to the randomness parameter of the search space, which represents the exploration of the method, avoiding stagnation or premature convergence. The FA generates the values that the parameters of the membership functions take for optimization use in the fuzzy systems for control. The control plants have one or more input variables that are processed and result in one or more output variables, it would be very difficult to model the human reasoning in equations to achieve a machine acquires the knowledge acquired by humans. For that reason the fuzzy logic that generates that insertity is used as if it were human reasoning.
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References
X.S. Yang, X. He, Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1(1), 36 (2013)
X.-S. Yang, Nature-Inspired Metaheuristic Algorithms. (Luniver Press, 2010)
L. Amador-Angulo, O. Castillo, Comparative analysis of designing differents types of membership functions using bee colony optimization in the stabilization of fuzzy controllers, in Nature Inspired Design of Hybrid Intelligent Systems, vol. 667 (Springer, Berlin, 2017), pp. 551–571
M.L. Lagunes, O. Castillo, J. Soria, Methodology for the optimization of a fuzzy controller using a bio-inspired algorithm. Fuzzy Log. Intell. Syst. Des. 648, 131–137 (2017). Springer
E. Bernal, O. Castillo, J. Soria, Imperialist competitive algorithm with dynamic parameter adaptation applied to the optimization of mathematical functions. Nat. Inspired Des. Hybrid Intell. Syst. 667, 329–341 (2017). Springer
L. RodrÃguez, O. Castillo, J. Soria, A study of parameters of the grey wolf optimizer algorithm for dynamic adaptation with fuzzy logic. Nat. Inspired Des. Hybrid Intell. Syst. 667, 371–390 (2017). Springer
C. Peraza, F. Valdez, O. Castillo, Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm. Fuzzy Log. Augment. Neural Optim. Algorithms 749, 29–37 (2018). Springer
M.L. Lagunes, O. Castillo, F. Valdez, J. Soria, P. Melin, Parameter optimization for membership functions of type-2 fuzzy controllers for autonomous mobile robots using the firefly algorithm. Fuzzy Inf. Process. 831, 569–579 (2018). NAFIPS
M.L. Lagunes, O. Castillo, J. Soria, Optimization of membership function parameters for fuzzy controllers of an autonomous mobile robot using the firefly algorithm. Fuzzy Log. Augment. Neural Optim. Algorithms 749, 199–206 (2018). Springer
L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
L.A. Zadeh, Fuzzy logic, Computer (Long. Beach. Calif), vol. 21, no. 4, pp. 83–93, (Apr. 1988)
L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-III. Inf. Sci. (Ny) 9(1), 43–80 (1975)
N.N. Karnik, J.M. Mendel, Q. Liang, Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7(6), 643–658 (1999)
Q. Liang, J.M. Mendel, Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)
O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz, Type-2 fuzzy logic: theory and applications, in 2007 IEEE International Conference on Granular Computing (GRC 2007), (2007), pp. 145–145
J. Pérez, F. Valdez, O. Castillo, Modification of the bat algorithm using type-2 fuzzy logic for dynamical parameter adaptation. Nat. Inspired Des. Hybrid Intell. Syst. 667, 343–355 (2017). Springer
C. Soto, F. Valdez, O. Castillo, A review of dynamic parameter adaptation methods for the firefly algorithm. Nat. Inspired Des. Hybrid Intell. Syst. 667, 285–295 (2017). Springer
C. Solano-Aragón, O. Castillo, Optimization of benchmark mathematical functions using the firefly algorithm. Recent. Adv. Hybrid Approaches Des. Intell. Syst. 547, 177–189 (2014). Springer
P. Ochoa, O. Castillo, J. Soria, Fuzzy differential evolution method with dynamic parameter adaptation using type-2 fuzzy logic, in Intelligent Systems, 8th International Conference on, IEEE, (2016), pp. 113–118
Water Level Control in a Tank—MATLAB & Simulink Example—MathWorks America Latina. [Online]. Available: https://la.mathworks.com/help/fuzzy/examples/water-level-control-in-a-tank.html. Accessed 04 Jul 2018
Temperature Control in a Shower—MATLAB & Simulink—MathWorks America Latina. [Online]. Available: https://la.mathworks.com/help/fuzzy/temperature-control-in-a-shower.html. Accessed 04 Jul 2018
P. Melin, C.I. González, J.R. Castro, O. Mendoza, O. Castillo, Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)
C.I. González, P. Melin, J.R. Castro, O. Castillo, O. Mendoza, Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)
C.I. González, P. Melin, J.R. Castro, O. Mendoza, O. Castillo, An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)
E. Ontiveros, P. Melin, O. Castillo, High order α-planes integration: a new approach to computational cost reduction of general Type-2 fuzzy systems. Eng. Appl. AI 74, 186–197 (2018)
C. Leal RamÃrez, O. Castillo, P. Melin, A. RodrÃguez DÃaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)
N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via fuzzy lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. of AI 25(5), 971–979 (2012)
O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control 4(4), 771–783 (2008)
G.M. Mendez, O. Castillo, Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm, Fuzzy Systems, 2005, in The 14th IEEE International Conference on FUZZ’05, 230–235
P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electr. 48(5), 951–955
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Lagunes, M.L., Castillo, O., Valdez, F., Soria, J. (2020). Comparison of Fuzzy Controller Optimization with Dynamic Parameter Adjustment Based on of Type-1 and Type-2 Fuzzy Logic. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_4
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