Boscovich Fuzzy Regression Line

Škrabánek, Pavel; Marek, Jaroslav; Pozdílková, Alena

doi:10.3390/math9060685

Cited by 8 publications

(2 citation statements)

References 50 publications

(112 reference statements)

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“…In this study, R programming language is used with R-Studio version 1.4.1106 to obtain the fuzzy regression results. We have utilized the PLRLS method of the fuzzyreg software package [24,25].…”

Section: Fuzzy Regressionmentioning

confidence: 99%

Handling uncertainty in rheological properties of green eggshell nanocomposites by a fuzzy-hybrid modeling approach: a comparative study

et al. 2023

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Fuzzy regression is an attractive application due to its ability to solve problems in the absence of accurate mathematical models. In this study, rheological properties of a bio-nanocomposite were predicted using fuzzy regression. The aim of study is to introduce the methodology and application of fuzziness in detail for handling uncertainty in the experimental data. In this context, rheological properties of green eggshell bio-nanocomposites are investigated using different operating parameters. Since the determination of surface tension is prone to experimental errors, a novel fuzzy-hybrid model is proposed in addition to a crisp model. Statistical error analysis is conducted to assess the validity of the multivariate models proposed. While both models provided satisfactory results, it is observed that the fuzzy-hybrid model outperforms the crisp model in both accuracy and generality. This study highlights that the proposed models have an impressive potential to be used for modeling experimental studies in nanotechnological manufacturing applications.

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Section: Fuzzy Regressionmentioning

confidence: 99%

Handling uncertainty in rheological properties of green eggshell nanocomposites by a fuzzy-hybrid modeling approach: a comparative study

et al. 2023

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“…A wide variety of fuzzy linear models can be used for approximating a linear dependence according to a set of observations in fuzzy regression analysis. (Skrabanek & Marek [8]). It can also aid in reducing the interference of unnecessary information, thereby improving the precision of the results (Kang et al, [3]).…”

Section: Introductionmentioning

confidence: 99%

Computation of Fuzzy Linear Regression Model using Simulation Data

Aliya Syaffa Zakaria,

Muhammad Ammar Shafi,

Mohd Arif Mohd Zim

et al. 2024

ARASET

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Regression analysis is a powerful technique for determining the causal influence on population outcome, although it is susceptible to outliers. It also oversimplifies the real-world data and problem, as data is rarely linearly separable. This paper analyses the simulation data of a fuzzy linear regression model (FLRM) model, which can aid in minimizing the interference of unwanted information, hence improving the precision of results. The aim of the fuzzy linear regression model (FLRM) is used to determine the best prediction model with the lowest measurement error. The model provides a fundamental mathematical and statistical framework for acknowledging the data imprecision. This study has applied the fuzzy linear regression model (FLRM) to evaluate the data in 15 rows of respondent models. The study implemented measurement error of cross-validation technique which is mean square error (MSE) and root mean square error (RMSE), to enhance data accuracy. Microsoft Excel and Matlab were applied to obtain the result. The simulation result indicates that comparing models with two measurement errors should be used to determine the optimal results. MSE and RMSE value with six parameters of a degree of fitting H-value have been calculated in the study. It concludes that a degree of fitting H-value of 0.0 is proven as a good model with the lowest MSE and RMSE measurement errors.

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Extension-Principle-Based Approach to Least Square Fuzzy Linear Regression

Stanojević

2022

Intelligent Methods Systems and Applications in Computing, Communications and Control

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Boscovich Fuzzy Regression Line

Cited by 8 publications

References 50 publications

Handling uncertainty in rheological properties of green eggshell nanocomposites by a fuzzy-hybrid modeling approach: a comparative study

Handling uncertainty in rheological properties of green eggshell nanocomposites by a fuzzy-hybrid modeling approach: a comparative study

Computation of Fuzzy Linear Regression Model using Simulation Data

Extension-Principle-Based Approach to Least Square Fuzzy Linear Regression

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