Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/970931 |
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| author | Yi-Chung Hu |
| author_facet | Yi-Chung Hu |
| author_sort | Yi-Chung Hu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d577b0485b8d433abbd173bdd7bbaa02 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d577b0485b8d433abbd173bdd7bbaa022025-02-03T05:44:06ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/970931970931Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic AlgorithmsYi-Chung Hu0Department of Business Administration, Chung Yuan Christian University, Chung Li 32023, TaiwanOn 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.http://dx.doi.org/10.1155/2014/970931 |
| spellingShingle | Yi-Chung Hu Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms The Scientific World Journal |
| title | Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms |
| title_full | Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms |
| title_fullStr | Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms |
| title_full_unstemmed | Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms |
| title_short | Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms |
| title_sort | multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms |
| url | http://dx.doi.org/10.1155/2014/970931 |
| work_keys_str_mv | AT yichunghu multilayerperceptronforrobustnonlinearintervalregressionanalysisusinggeneticalgorithms |