A New Approach for Interval Type-3 Fuzzy Control of Nonlinear Plants

Amador-Angulo, Leticia; Castillo, Oscar; Castro, Juan R.; Melín, Patricia

doi:10.1007/s40815-023-01470-9

Cited by 14 publications

(2 citation statements)

References 42 publications

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“…For the proposed method, the complexity of the evaluation of each individual lies in the use of Type-3 fuzzy inference systems. The complexity of a Type-3 fuzzy model was described in [47], where it was determined that complexity based on the vertical-slices theory for centroid type reduction is approximately O(NKL). Where it is assumed that a primary variable x is sampled into N points, K is the number of iterations to approximate a switch point, and L means samples for a vertical slice.…”

Section: Discussionmentioning

confidence: 99%

“…This number is multiplied by 21 because each Type-3 Trapezoidal MF has seven parameters (p a1 , p b1 , p c1 , p d1 , p λ , p ↕1 , and p ↕2 ) and there are three Type-3 Trapezoidal MF in each fuzzy input. Three membership functions are used because previous works have shown that this number of membership functions allows good results for classification problems [30,31], as well as in other applications [47]. The three constants of the output are added to this multiplication, and finally, the multiplication of TR by 2 (consequents and activation status).…”

Section: Parameters Minimum Maximummentioning

confidence: 99%

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Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

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An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

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Section: Discussionmentioning

confidence: 99%