Fuzzy nonparametric regression based on local linear smoothing technique

Wang, Ning; Zhang, Wenxiu; Mei, Changlin

doi:10.1016/j.ins.2007.03.002

Cited by 68 publications

(24 citation statements)

References 31 publications

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“…The mean of distribution is zero and the standard deviations are respectively τ = 0.1R m and τ = 0.1R m with R m , R m being the ranges of m( Example 5.3. Consider the below function from [31].…”

Section: Numerical Resultsmentioning

confidence: 99%

Integrating Ridge-type regularization in fuzzy nonlinear regression

Farnoosh¹,

Ghasemian²,

Fard³

2012

Comput. Appl. Math.

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Abstract. In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the number of basis functions and smoothing parameter. In order to select tuning parameters in the regularization method, we use the Hausdorff distance for fuzzy numbers which was first suggested by Dubois and Prade [8]. The cross-validation procedure for selecting the optimal value of the smoothing parameter and the number of basis functions are fuzzified to fit the presented model. The simulation results show that our fuzzy nonlinear modelling performs well in various situations.Mathematical subject classification: Primary: 62J86; Secondary: 62J07.

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Section: Numerical Resultsmentioning

confidence: 99%

Integrating Ridge-type regularization in fuzzy nonlinear regression

Farnoosh¹,

Ghasemian²,

Fard³

2012

Comput. Appl. Math.

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“…To this end, we have to extend our least square algorithms using fuzzy regression techniques (Hong & Hwang, 2004;Wang, Zhang, & Mei, 2007). …”

Section: Discussionmentioning

confidence: 99%

Applying adaptive prediction to sea-water quality measurements

Hatzikos

Hätönen

Bassiliades

et al. 2009

Expert Systems with Applications

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“…clusterwise regression analysis with r = 1) with fuzzy output and numeric inputs represents an interesting topic in the fuzzy regression literature. In fact, in literature this case has been extensively investigated from both the theoretical and applicative points of view; see, for instance, [5,7,23,19,15,8,9,59,13,18,34,14,16,17,37,56,11]. It could be interesting to suggest a new version of the class of FCWR-LR models for both LR fuzzy response variable and explanatory variables and also with fuzzy random dependent variable and/or fuzzy random explanatory variables [28] or to extend the switching regression analysis suggested by Yang et al [61] to fuzzy output and/or fuzzy inputs case.…”

Section: Final Remarks and Future Perspectivesmentioning

confidence: 99%