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The Scientific World Journal
Volume 2014, Article ID 970931, 8 pages
Research Article

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

Department of Business Administration, Chung Yuan Christian University, Chung Li 32023, Taiwan

Received 5 May 2014; Accepted 12 June 2014; Published 29 June 2014

Academic Editor: Chih-Chou Chiu

Copyright © 2014 Yi-Chung Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.