Interval Type-3 Fuzzy Differential Evolution for Parameterization of Fuzzy Controllers

Ochoa, Patricia; Castillo, Oscar; Melín, Patricia; Castro, Juan R.

doi:10.1007/s40815-022-01451-4

Cited by 13 publications

(2 citation statements)

References 31 publications

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“…In a recent work, a study by varying an important element of interval type-3 fuzzy sets, known as the LowerScale (λ) parameter, was performed. By manipulating this parameter, different proposed fuzzy systems are utilized to dynamically adjust a parameter of the differential evolution algorithm during its execution, aiming to enhance its convergence [46].…”

Section: Differential Evolutionmentioning

confidence: 99%

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

et al. 2023

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The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.

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Section: Differential Evolutionmentioning

confidence: 99%

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

et al. 2023

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“…Extended and unscented Kalman filters are proposed to optimize IT3 FLS parameters. In [61], the performance-emissions of a single-cylinder compression ignition engine with diesel hydrogen dual fuel were predicted.…”

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confidence: 99%

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Castorena,

Méndez,

López-Juárez

et al. 2024

Mathematics

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This paper presents the novel enhanced Wagner–Hagras interval type-3 Takagi–Sugeno–Kang fuzzy logic system with type-1 non-singleton inputs (EWH IT3 TSK NSFLS-1) that uses the backpropagation (BP) algorithm to train the antecedent and consequent parameters. The proposed methodology dynamically changes the parameters of only the alpha-0 level, minimizing some criterion functions as the current information becomes available for each alpha-k level. The novel fuzzy system was applied in two industrial processes and several fuzzy models were used to make comparisons. The experiments demonstrated that the proposed fuzzy system has a superior ability to predict the critical variables of the tested processes with lower prediction errors than those produced by the benchmark fuzzy systems.

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Review of Type-3 Fuzzy Control

Castillo,

Melin

2023

SpringerBriefs in Applied Sciences and Technology

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Interval Type-3 Fuzzy Differential Evolution for Parameterization of Fuzzy Controllers

Cited by 13 publications

References 31 publications

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Review of Type-3 Fuzzy Control

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