&lt;title&gt;Hybrid fuzzy regression with trapezoidal fuzzy data&lt;/title&gt;

Razzaghnia, Tahereh; Danesh, Sedigheh; Maleki, A.

doi:10.1117/12.923172

Cited by 8 publications

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“…[1,6,11,12,15,26,31])). In the fuzzy literature, several extensions of this method has been proposed [20,29,30], occasionally in a non-parametric context [4,13,32,39]. In recent years, the prediction of the regression parameters has gained a great attention among the researchers of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…In general, fuzzy regression techniques can be classified into two distinct areas: linear programming-based method that minimizes the total spread of the output, is named possibility regression (see, e.g. [27,28,29,30,32,34,35,36,37,38]) and fuzzy least squares method that minimizes the total square error of the output is called the fuzzy least square method (FLSM) (see, e.g. [1,6,11,12,15,26,31])).…”

Section: Introductionmentioning

confidence: 99%

“…[27,28,29,30,32,34,35,36,37,38]) and fuzzy least squares method that minimizes the total square error of the output is called the fuzzy least square method (FLSM) (see, e.g. [1,6,11,12,15,26,31])). In the fuzzy literature, several extensions of this method has been proposed [20,29,30], occasionally in a non-parametric context [4,13,32,39].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regression parameters prediction in data set with outliers using neural network

Razzaghnia¹

2019

Hacettepe Journal of Mathematics and Statistics

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Popular regression techniques often suffer at the presence of data outliers. The different methods have proposed to make smaller the effect of the outlier on the parameter estimates. In this study, an algorithm has been addressed based on Adaptive network based fuzzy inference system to define the unknown parameters of regression model where dependent variable has outlier. So, three numerical examples are solved to test the activity of the proposed algorithm in regression model estimation. Also, the obtained results from the different methods, such as linear programming (LP) and fuzzy weights with linear programming (FWLP) are compared together. The results show that the proposed method is not to be affected the outliers in the solving process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression parameters prediction in data set with outliers using neural network

Razzaghnia¹

2019

Hacettepe Journal of Mathematics and Statistics

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“…Moreover, the several variants FLS (see, e.g. [1,5,24,29]) and mathematical programming (see, e.g. [27,28]) have been applied for the fuzzy linear regression problem.…”

Section: Introductionmentioning

confidence: 99%

“…For fuzzy nonparametric regression, Cheng and Lee [6] proposed k-NN and kernel smoothing techniques, Farnoosh et al [14] introduced a modification on ridge estimation and Wang et al [40] proposed local linear smoothing technique. Razzaghnia and Danesh [30] analyzed local linear smoothing technique in nonparametric regression with trapezoidal fuzzy data. In the recent years, the artificial intelligent modeling techniques have been utilized to approximate the non-linear problems, the complex behaviors and the prediction of the regression parameters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Parameters Estimation via Hybrid Methods

Danesh¹

2016

HJMS

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Fuzzy regression analysis is one of the most widely used statistical techniques which represents the relation between variables. In this paper, the crisp inputs and the symmetrical triangular fuzzy output are considered. Two hybrid algorithms are considered to fit the fuzzy regression model. In this study, algorithms are based on adaptive neuro-fuzzy inference system. The results are derived based on the V-fold cross validation, so that the validity and quality of the suggested methods can be guaranteed. Finally, using the numerical examples, the performance of the suggested methods are compared with the other ones, such as linear programming (LP) and quadratic programming (QP). Based on examples, hybrid methods are verified for the prediction.

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Optimal design of adaptive neuro-fuzzy inference system using PSO and ant colony optimization for estimation of uncertain observed values

Danesh,

Danesh

2023

Soft Comput

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This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC𝑂 𝑅 . In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

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<title>Hybrid fuzzy regression with trapezoidal fuzzy data</title>

Cited by 8 publications

References 0 publications

Regression parameters prediction in data set with outliers using neural network

Regression parameters prediction in data set with outliers using neural network

Fuzzy Parameters Estimation via Hybrid Methods

Optimal design of adaptive neuro-fuzzy inference system using PSO and ant colony optimization for estimation of uncertain observed values

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