Fuzzy robust regression analysis

Watada, Junzo; Yabuuchi, Yoshiyuki

doi:10.1109/fuzzy.1994.343612

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…Traditional regression theory considers error as a concept of probability, whereas fuzzy regression theory see errors as a possibility [4,5]. Thus, the linear fuzzy regression model can be expressed as:…”

Section: Fuzzy Regressionmentioning

confidence: 99%

Using Fuzzy Regression and Neural Network to Predict Organizational Performance

Lin¹

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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As everyone knows, multiple regression analysis is an important approach to prediction studies. However, regression model has some limitations and constraints in the real world practices. This study applied fuzzy regression using neural network (FRNN) to predict organizational performance, and the findings indicate that the accuracy rate analysis supported FRNN to be a better method to predict nonlinear variables.

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Section: Fuzzy Regressionmentioning

confidence: 99%

Using Fuzzy Regression and Neural Network to Predict Organizational Performance

Lin¹

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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“…Because the regression coefficients are fuzzy numbers, the estimated dependent variable will be a fuzzy number. Fuzzy linear regression approaches have been successfully applied in cost estimation of wastewater treatment (Wen & Lee, 1999), financial forecasting (Oh et al, 1990), earthquake prediction (Bardossy et al, 1992), sales volume estimation (Watada, 1992), and ergonomics (Chang et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Optimization of multiple responses in the Taguchi method using fuzzy regression

Al–Refaie

2014

AIEDAM

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In reality, the behavior of processes is sometimes vague and the observed data is irregular. This research proposes an approach for optimizing fuzzy multiple responses using fuzzy regression. In this approach, each response repetition is transformed into a signal to noise ratio then modeled using statistical multiple regression. A trapezoidal fuzzy regression model is formulated for each response utilizing the statistical regression coefficients. The most desirable response values and the deviation function are determined for each response. Finally, four optimization models are formulated for the trapezoidal membership fuzzy number to obtain the optimal factor level at each number. Two case studies are adopted for illustration, where excluding response fuzziness will result in misleading optimal factor settings if solved by the traditional optimization techniques. In conclusion, the proposed approach based on fuzzy regression approach can successfully optimize fuzzy multiple responses in a wide range of manufacturing applications on the Taguchi's method. Moreover, compared to other approaches, such as data envelopment analysis and grey relational analysis, the proposed approach has the distinct advantage of being able to generate models using only a small number of experimental data sets and minimizing inherent variations.

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“…Interval regression analysis, which provides interval estimation of individual dependent variables, is an important tool for dealing with uncertain data [ 1 – 3 ]. Interval regression analysis was developed on the basis of an important tool, namely, fuzzy regression analysis introduced by Tanaka et al [ 4 ], whose objective is to build a model that contains all observed output data in terms of fuzzy numbers [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

2014

The Scientific World Journal

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On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

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Fuzzy robust regression analysis

Cited by 13 publications

References 3 publications

Using Fuzzy Regression and Neural Network to Predict Organizational Performance

Using Fuzzy Regression and Neural Network to Predict Organizational Performance

Optimization of multiple responses in the Taguchi method using fuzzy regression

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

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