Fuzzy Nonlinear Regression Model Based on LS-SVM in Feature Space

Hong, Dug Hun; Hwang, Chong-Sun

doi:10.1007/11881599_23

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Lin et al 22 developed a method of systematic selection of parameters e and Gaussian kernel width. Other authors [22][23][24][25] have used heuristic methods for the selection of these parameters and have used SA to find the two parameters C and e. There is no clear rule or existing method for the selection of such parameters. Indeed, every case is unique because its database is unique.…”

Section: Fuzzy Linear Svrmentioning

confidence: 99%

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

Megri

Djabri

2016

Indoor and Built Environment

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The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

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Section: Fuzzy Linear Svrmentioning

confidence: 99%

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

Megri

Djabri

2016

Indoor and Built Environment

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“…Progress in fuzzy mathematics encourages engineering and other scientists in application fields for presentation of real models such as fuzzy image filtering [1], fuzzy clustering [2,3], fuzzy multivariable nonlinear regression analysis [2] and fuzzy classification [3,4] (Classification is among the most important problem tasks in the realm of data analysis, data mining and machine learning and has many applications in industry, including, e.g., oil spill detection [5], intrusion detection in computer networks [6], breast cancer detection [7], fingerprint identification [8], text document classification [9,10], handwritten Tamil character recognition [11], Epo doping control [12], human identification [13,14] and signature verification [15]). …”

Section: Introductionmentioning

confidence: 99%

An extension to fuzzy support vector data description (FSVDD*)

Forghani

Yazdi

Effati

2011

Pattern Anal Applic

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The well-known support vector data description (SVDD) is based on precise description of precise data. When we know the features of training samples precisely and we are uncertain about their class labels, the fuzzy SVDD can be used to obtain the data description. But if the features of training samples are fuzzy numbers, the fuzzy SVDD cannot be utilized. In this paper, we extend the fuzzy SVDD for the description of such training samples and then apply our proposed method, called FSVDD*, to real data. The experimental results show the ability of the proposed method in Taiwanese tea evaluation.

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“…A collection of relevant papers dealing with several approaches to fuzzy regression analysis can be found in [7]. In contrast to the fuzzy linear regression, there have been only a few articles on fuzzy nonlinear regression [8][9][10][11][12][13][14][15][16]. In this paper, we discuss multivariate fuzzy nonlinear regression by support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression with fuzzy target output

Forghani

Yazdi

Effati

2011

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE)

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In this paper, we incorporate the concept of fuzzy set theory into the support vector regression (SVR). In our proposed method, target outputs of training samples are considered to be fuzzy numbers and then, membership function of actual output (objective hyperplane in high dimensional feature space) is obtained. Two main properties of our proposed method are: (1) membership function of actual output can be obtained without pre-assumption on type of membership function of the bias term and the components of weight vector; (2) the membership function of target output can be each type of fuzzy number.

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Fuzzy Nonlinear Regression Model Based on LS-SVM in Feature Space

Cited by 8 publications

References 14 publications

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

An extension to fuzzy support vector data description (FSVDD*)

Support vector regression with fuzzy target output

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