Analysis and evaluation in a welding process applying a Redesigned Radial Basis Function

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• 作者：Rolando J. Praga-Alejo^a ; ^{rolando_praga_alejo@uadec.edu.mx} ; Luis M. Torres-Treviñ ; o^b ; ² ; ^{luis.torres.ciidit@gmail.com} ; David S. Gonz&#225 ; lez-Gonz&#225 ; lez^a ; ¹ ; ^{david.gonzalez@uadec.edu.mx} ; Jorge Acevedo-D&#225 ; vila^c ; ³ ; ^{jacevedo@comimsa.com} ; Francisco Cepeda-Rodrí ; guez^c ; ³ ; ^{fcocepeda@comimsa.com}
• 关键词：Radial Basis Function ; Genetic Algorithm ; Mahalanobis distance ; Hybrid Learning ; Residual Analysis
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：August, 2012
• 卷：39
• 期：10
• 页码：9669-9675
• 文件大小：363 K

摘要

The Hybrid Learning Process method proposed in this work, is applied to a Genetic Algorithm and Mahalanobis distance, instead of computing the centers matrix by Genetic Algorithm. It is determined in such a way as to maximize the coefficient of determination R² and the Fitness Function depends on the prediction accuracy fitted by the Hybrid Learning approach, where the coefficient of determination R² is a global metric evaluation. The Mahalanobis distance is a measurement of distance which uses the correlation between variables and takes into account the covariance and variance matrix in the input variables; this distance helps to reduce the variance into variables. The purpose of this work is to show a methodology to modify the Radial Basis Function and also improve the parameters and variables that are associated with Radial Basis Function learning processes; since the Radial Basis Function has mainly two problems, the Euclidean distance and the calculation of centroids. The results indicated that the statistical methods such as Residual Analysis are good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The principal conclusion of this work is that the Radial Basis Function Redesigned improved the accuracy of the model using a Hybrid Learning Process and the Radial Basis showed very good performance in a real case, considering the prediction of specific responses in a laser welding process.