2021
DOI: 10.3390/axioms10030194
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Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers

Abstract: This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. Previously, we have worked with both kinds of fuzzy systems in different types of benchmark problems and it has been found that the use of fuzzy logic in combination with the differential evolution algorithm gives good results. In some of the studies, it is clearly shown that, when compared to other… Show more

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Cited by 12 publications
(1 citation statement)
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“…Some examples are fault diagnosis, image segmentation, and medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs [22] and improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method [23]. As a difference from previous works, the contribution of this paper is the use of general type 2 fuzzy systems for parameter adaptation in differential evolution and its application to design a Sugeno controller.…”
Section: Introductionmentioning
confidence: 99%
“…Some examples are fault diagnosis, image segmentation, and medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs [22] and improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method [23]. As a difference from previous works, the contribution of this paper is the use of general type 2 fuzzy systems for parameter adaptation in differential evolution and its application to design a Sugeno controller.…”
Section: Introductionmentioning
confidence: 99%