Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

Pehlivan, Nimet Yapıcı; Apaydin, Ayşen

doi:10.1007/s40747-016-0013-9

Cited by 13 publications

(4 citation statements)

References 48 publications

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“…The fuzzy RBFNN can be The fuzzification process on fuzzy RBFNN model is similar to that on a hybrid of fuzzy and back propagation neural network (fuzzy NN) model. Fuzzifications on fuzzy RBFNN using α-level sets and fuzzy α -cut interval have been proposed respectively by [11] and [8]. To fuzzify the inputs of fuzzy NN, Wutsqa and Rahmada [12] implement a combination of fuzzy membership function and OR operator, while Senol and Yildrim [13] implement a fuzzy membership function and the fuzzy rule.…”

Section: Introductionmentioning

confidence: 99%

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Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

2021

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In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level cooccurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.

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Section: Introductionmentioning

confidence: 99%

“…Even GRR provides a global optimum solution that will be appropriate only for the small number of inputs. Back propagation (BP) is another alternative method used by Pehlivan and Apaydin [11]. BP has a limitation.…”

Section: Introductionmentioning

confidence: 99%

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

2021

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“…[2,3,4,5,6,7,8,9,10,11,12,13,14]), (2) fuzzy least squares and fuzzy least absolutes parametric/non-parametric methods, where the gap between the predicted fuzzy values and available fuzzy data is minimized with regard to various distance measures between two fuzzy numbers, covering the most commonly used linear and non-linear models (see for instance Refs. [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]), and (3) machine learning techniques, like evolutionary algorithms [30,31,32,33,34], support vector machines [35,36,37,38], and neural networks embedded in fuzzy regression analysis [39,40,41,42,43], where the ideas and terminology relevant to biological evolution are used, such as mutation, recombination, reproduction and selection. Here the candidate solutions of the optimization problem represent individuals in a population.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Linear Regression Model With Functional Predictors And Fuzzy Responses

Hesamian¹,

Mg²

2021

Preprint

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A novel functional regression model was introduced, where the predictor was a curve linked to a scalar fuzzy response variable. An absolute error-based penalized method with SCAD loss function was proposed to evaluate the unknown components of the model. For this purpose, a concept of fuzzy-valued function was developed and discussed. Then, a fuzzy large number notion was proposed to estimate the fuzzyvalued function. Some common goodness-of-fit criteria were also used to examine the performance of the proposed method. Efficiency of the proposed method was then evaluated through two numerical examples, including a simulation study and an applied example in the scope of watershed management. The proposed method was also compared with several common fuzzy regression models in cases where the functional data was converted to scalar ones.

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“…e authors in [55,56] used a random weight network to develop a fuzzy nonlinear regression model. A fuzzified radial basis function network for obtaining estimations of fuzzy regression models is utilized in [57]. For further papers on this topic, see [19,20,46,[58][59][60][61].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network

Karbasi

Rabiei

Nazemi

2020

Journal of Mathematics

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Bridge regression is a special family of penalized regressions using a penalty function ∑ A j γ with γ ≥ 1 that for γ = 1 and γ = 2 , it concludes lasso and ridge regression, respectively. In case where the output variable in the regression model was imprecise, we developed a bridge regression model in a fuzzy environment. We also exhibited penalized fuzzy estimates for this model when the input variables were crisp. So, we perform the presented optimization problem for the model that leads to a multiobjective program. Also, we try to determine the shrinkage parameter and the tuning parameter from the same optimization problem. In order to estimate fuzzy coefficients of the proposed model, we introduce a hybrid scheme based on recurrent neural networks. The suggested neural network model is constructed based on some concepts of convex optimization and stability theory which guarantees to find the approximate parameters of the proposed model. We use a simulation study to depict the performance of the proposed bridge technique in the presence of multicollinear data. Furthermore, real data analysis is used to show the performance of the proposed method. A comparison between the fuzzy bridge regression model and several other shrinkage models is made with three different well-known fuzzy criteria. In this study, we visualize the performance of the model by Taylor’s diagram and Bubble plot. Also, we examine the predictive ability of the model, thus, obtained by cross validation. The numerical results clearly showed higher accuracy of the proposed fuzzy bridge method compared to the other existing fuzzy regression models: fuzzy bridge regression model, multiobjective optimization, recurrent neural network, stability convergence, and goodness-of-fit measure.

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Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

Cited by 13 publications

References 48 publications

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

A Fuzzy Linear Regression Model With Functional Predictors And Fuzzy Responses

A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network

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