lfl: An R package for linguistic fuzzy logic

Burda, Michal; Štěpnička, Martin

doi:10.1016/j.fss.2021.07.007

Cited by 10 publications

(3 citation statements)

References 66 publications

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“…Fuzzy logic (FL) is another method that uses the classical fuzzy sets mathematical theory. , Fuzzy logic uses membership functions (MFs) to evolve between real-world and fuzzy models. pimf, zmf, and smf are the common MFs .…”

Section: Machine Learning Models Used For Gas-hydrate-related Studiesmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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Section: Machine Learning Models Used For Gas-hydrate-related Studiesmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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“…Regarding the second group, we cite the following R packages: FuzzyNumbers (Gagolewski and Caha, 2019), sets (Meyer and Hornik, 2009), FuzzyR (Chen et al, 2020), and lfl (Burda and S̆tĕpnic̆ka, 2022). FuzzyNumbers focuses on a particular subclass of fuzzy sets named fuzzy numbers.…”

Section: Related Workmentioning

confidence: 99%

fsr: An R package for fuzzy spatial data handling

Carniel

Venâncio

Schneider

2023

Transactions in GIS

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Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r-project.org/package=fsr. Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations.

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“…Its author is Radek Valášek. Let us remark that in the R-repository is available R-package lfl providing algorithms used in this paper, see [19]. ).…”

Section: Detection Of Structural Breaksmentioning

confidence: 99%

A Combination of Fuzzy Techniques and Chow Test to Detect Structural Breaks in Time Series

Novák

Truong

2023

Axioms

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In a series of papers, we suggested a non-statistical method for the detection of structuralbreaks in a time series. It is based on the applications of special fuzzy modeling methods, namelyFuzzy transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). In this paper, wecombine our method with the principles of the classical Chow test, which is a well-known statisticalmethod for testing the presence of a structural break. The idea is to construct testing statistics similarto that of the Chow test which is formed from components of the first-degree F-transform. Thesecomponents contain an estimation of the average values of the tangents (slopes) of the time seriesover an imprecisely specified time interval. In this paper, we illustrate our method and its statisticaltest on a real-time series and compare it with three classical statistical methods.

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lfl: An R package for linguistic fuzzy logic

Cited by 10 publications

References 66 publications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

fsr: An R package for fuzzy spatial data handling

A Combination of Fuzzy Techniques and Chow Test to Detect Structural Breaks in Time Series

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