Dynamical ramp for nonlinear phenomena control in DC-DC converters. Journal of Electrical Systems, Banerjee S, Verghese GC. Nonlinear phenomena in power electronics. Erickson R, Maksimovic D. Fundamentals of power electronics. Hagras H. Type-2 FLCs: A new generation of fuzzy controllers.
Mendel JM. Type-2 fuzzy sets and systems. Atacak I, Bay O. A type-2 fuzzy logic controller design for buck and boost DC-DC converters. Journal of intelligent manufacturing, Type-2 Fuzzy sets: some questions and answers. Castillo O, Melin P. Recent advances in interval type-2 fuzzy systems. Springer in Computational Intelligence. Castillo O. See our disclaimer. Customer Reviews. Write a review. See any care plans, options and policies that may be associated with this product.
Advances in Type-2 Fuzzy Sets and Systems
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Recent Advances in Interval Type-2 Fuzzy Systems | SpringerLink
All Rights Reserved. Cancel Submit. In recent years, the applications of type-2 papers used the optimization based design to employ fuzzy systems in intelligent control have become a com- T2FLC in real-world environment. The following literature mon practice. GAs, PSO, ACO based design of T2FLC according to the different opti- have been used in automatic design of type-1 as well as mization methods mentioned previously is shown in type-2 systems that made it to become a standard practice Fig.
Now the trend has been extended to the use of the are in the front line in optimization of T2FLS.
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This is due solving a problem. This method is referred to as HO. This is because using optimization method is increasing yearly and this all of the reviewed methods have a record as successful trend is expected to continue in the future because T2FLSs methods of optimization of T2FLS in some applications. Although the HO method has outperformed some con- Based on this review, it is noteworthy to state that, to the ventional optimization methods such as genetic algo- best of our knowledge, most of the applications in the area rithms, PSO and firefly algorithm in different applications, of T2FLS use the IT2FLS and only few applications con- it cannot be declared as the best since it is not compared sidered the use of GT2FLS like [27, 33, 34].
For example, with all of the methods. We suggest was justified. This is due to the complexity in the design. Qilian L, Mendel JM Interval type-2 fuzzy logic systems: rithm, bee colony optimization, simulated annealing, firefly theory and design. Also, we recommend the optimization of T2FLC logic systems.
In: IEEE world congress on computational using flower pollination algorithm in view of the fact that it intelligence.
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The IEEE international conference on fuzzy sys- shows performance improvement over many meta-heuristic tems proceedings, 4—9 May, vol , pp — It is expected that these optimization Mendel JM General type-2 fuzzy logic systems made methods as well as those that will be proposed in the future simple: a tutorial. Castillo O, Melin P Optimization of type-2 fuzzy systems based on bio-inspired methods: a concise review.
Inf Sci — ISA Trans 53 5 — Meta-heuristic tems optimization with RNA genetic algorithm for double optimization algorithms have been used in order to solve inverted pendulum. Appl Math Model 39 1 — A review learning type-2 fuzzy logic controller design for the iRobot on optimizing the design of T2FLCs was presented in create robot. In: International conference on individual and this paper.
This review is to justify the need of opti- collective behaviors in robotics ICBR , 15—17 December, mization of parameters associated with type-2 fuzzy pp 15— To date, the most frequently used optimiza- Chiroma H, Abdulkareem S, Herawan T Evolutionary neural network model for West Texas intermediate crude oil tion methods in this area of research are genetic algo- price prediction. Appl Energy — rithms, particle swarm optimization and HO. The purpose PLoS One 10 8 :e research and explore other techniques that have received Castillo O, Melin P A review on interval type-2 fuzzy little or no attention.
Eng Appl Artif No. Intell —44 Pulido M, Melin P, Castillo O Particle swarm opti- mization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican stock exchange. Inf Sci References — Mendel JM Type-2 fuzzy sets and systems: an overview. Springer, New York Kumbasar T, Hagras H Big bang—big crunch optimiza- 3. Zadeh LA Fuzzy sets. Inf Control 8 3 — tion based interval type-2 fuzzy PID cascade controller design 4. Manag Sci 17 4 :B—B Castillo O, Melin P, Kacprzyk J, Pedrycz W Type-2 foraging optimization approach for tuning type-2 fuzzy logic fuzzy logic: theory and applications.
In: IEEE international controller. Wagner C, Hagras H Toward general type-2 fuzzy logic Improving the performance of the Egyptian second testing systems based on zSlices. Wagner C, Hagras H zSlices—towards bridging the gap troller tuned by modified biogeography-based optimization. IEEE, Astudillo L, Melin P, Castillo O Nature inspired chem- pp — ical optimization to design a type-2 fuzzy controller for a mobile Liu F An efficient centroid type-reduction strategy for robot.
El-Nagar AM, El-Bardini M Interval type-2 fuzzy neural fuzzy inference systems: analysis, design and computational network controller for a multivariable anesthesia system based aspects. In: IEEE international fuzzy systems conference on a hardware-in-the-loop simulation. IEEE, pp 1—6 61 1 :1— Coupland S, John R Geometric type-1 and type-2 fuzzy Mendel JM Advances in type-2 fuzzy sets and systems.
Comput Biol Med. Mendel JM Uncertain rule-based fuzzy logic system: systems for sea water level prediction. In: IEEE sixth international introduction and new directions. Castillo O Introduction to type-2 fuzzy logic control. In: Appl Soft Comput applications.
General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial
Springer, New York, pp 3—5 — Dongrui W Approaches for reducing the computational Melin P, Castillo O A review on type-2 fuzzy logic cost of interval type-2 fuzzy logic systems: overview and applications in clustering, classification and pattern recognition. Melin P, Castillo O A review on the applications of type- Expert type-reduction. Castillo O, Melin P A review on the design and opti- doi Appl Soft Comput In: IEEE world congress on computational IEEE international conference on fuzzy systems fuzzy systems for controlling a mobile robot and a performance proceedings.
IEEE, pp — comparison with interval type-2 and type-1 fuzzy systems. Mendel JM On type-reduction versus direct defuzzifi- Expert Syst Appl 42 14 — cation for type-2 fuzzy logic systems. Kumbasar T, Hagras H A self-tuning zSlices-based design type-1 and type-2 fuzzy logic controllers.
Int J Mach general type-2 fuzzy PI controller. IEEE Trans logic systems made simple. Zhang Z, Zhang S Type-2 fuzzy soft sets and their Mendel JM Fuzzy logic systems for engineering: a applications in decision making.
J Appl Math Proc IEEE 83 3 — Hassanzadeh I, Mobayen S Controller design for rotary approximation, and orthogonal least-squares learning. IEEE inverted pendulum system using evolutionary algorithms. Math Trans Neural Netw 3 5 — Castillo O Type-2 fuzzy logic in intelligent control learning from examples. Springer, New York 22 6 — Linda O, Manic M General type-2 fuzzy c-means algo- trollers. Springer, Berlin, pp — Kumbasar T A simple design method for interval type-2 type-2 fuzzy sets. Inf Sci 3 — fuzzy PID controllers. Kumbasar T Robust stability analysis and systematic Kumbasar T Robust stability analysis of PD type single interval type-2 fuzzy logic controllers for a perturbed autono- input interval type-2 fuzzy control systems.
In: IEEE interna- mous wheeled mobile robot using genetic algorithms. IEEE, 13 — pp — In: Murgante B, Misra S, logic controllers. Inf Sci —2 In: Proceedings of the sixth international sym- mization using a hierarchical genetic algorithm applied to pat- posium on micro machine and human science, New York, NY, tern recognition. Eberhart RC, Shi Y Particle swarm optimization: genetic algorithms and particle swarm optimization. Expert Syst developments, applications and resources. In: Proceedings of the Appl 38 9 — IEEE, pp 81—86 Clerc M, Kennedy J The particle swarm—explosion, Computational intelligence techniques with application stability, and convergence in a multidimensional complex space.
Neural Netw World 23 6 — Simul Model Pract Theory 17 10 — genetic and hybrid optimization algorithms. Comput Mater Sci Xia W, Wu Z An effective hybrid optimization approach class of nonlinear chaotic systems. J Intell Fuzzy Syst for multi-objective flexible job-shop scheduling problems.
Appl Soft tions. Appl Soft Comput 13 1 — Comput 8 2 — Int J Mach Appl Soft Comput 7 2 — Math Probl Eng. Springer, New York, pp 19—47 control using type-2 fuzzy controller with species-DE-activated Wiley, continuous ACO.
New York doi Cervantes L, Castillo O Type-2 fuzzy logic aggregation algorithms. Springer, New York of multiple fuzzy controllers for airplane flight control. Inf Sci Herrera F Genetic fuzzy systems: taxonomy, current — research trends and prospects. Evol Intel 1 1 —46 Appl Soft methods for Mamdani-type fuzzy rule-based systems: designing Comput — Int J Approx Reason Castillo O, Cervantes L Genetic design of optimal type-1 52 6 — and type-2 fuzzy systems for longitudinal control of an airplane.