Development of Fuzzy Regression Models Using Genetic Algorithms

Mogilenko, A.V.; Pavlyuchenko, D. A.; Manusov, V.Z.

doi:10.1142/s021848850300217x

Cited by 7 publications

(4 citation statements)

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“…The simplex method is proposed as it has commonly been used on developing fuzzy regressions [43]. In VS-FR-GA, the genetic algorithm is used to determine the fuzzy spread parameters, as it is not susceptible to a lack of convexity of the solution landscapes [44] and also it can determine better fuzzy coefficients for generating more accurate models, compared with the simplex method [45]. The framework of the VS-FR-SM and VS-FR-GA are summarized in Figure 5.…”

Section: B Proposed Fuzzy Regression With Varying Spreadmentioning

confidence: 99%

Varying Spread Fuzzy Regression for Affective Quality Estimation

Chan

Engelke

2017

IEEE Trans. Fuzzy Syst.

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Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state-of-art and the recently-developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria which address both predictions of magnitudes and fuzziness.

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Section: B Proposed Fuzzy Regression With Varying Spreadmentioning

confidence: 99%