The fuzzy filter based on the method of areas’ ratio

Bobyr, M. V.; Milostnaya, N. A.; Булатников, В. А.

doi:10.1016/j.asoc.2022.108449

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…The use of a rigid minimum in traditional fuzzy inference algorithms does not allow obtaining satisfactory indicators of model accuracy. As it was shown in [11], dead zones of the ML model are formed. In that work a method of teaching MISO-fuzzy (Multiple Input Single Output) systems was reviewed and the results of numerical modeling were shown.…”

Section: Introductionmentioning

confidence: 89%

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Spline Optimization of Soft Connectives in Machine Learning Models

Kalnitsky,

Vilkov

2024

CLEIej

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In this study, the problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. All these problems have been solved by the authors. We have proposed an approximation of the signum function by a ?1-smooth spline. The first part of the spline is responsible for the curvature of the connective at the diagonal and is adjustable. The second part of the spline is the solution to the optimization problem. We minimized the difference between the connective and the associative connective, the latter in our study was the minimum function. In the resulting solution, the rate of deviation reduction is the highest among known connectives. We have achieved not only a small deviation from associativity, but also the presence of a large domain of exact associativity. This area is up to a third of the volume of all triples of arguments. A comparative analysis of the currently used soft connectives with the constructed model was carried out. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks.

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Section: Introductionmentioning

confidence: 89%

“…This class of soft connectives is much narrower than generalized averages. However, they have performed well in ML models [11].…”

Section: Definition 3 the Membership Function Of The -Operation Of Tw...mentioning

confidence: 99%