Name
Title | ||
|---|---|---|
Analysis and evaluation in a welding process applying a Redesigned Radial Basis Function |
Abstract | ||
|---|---|---|
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 R^2 and the Fitness Function depends on the prediction accuracy fitted by the Hybrid Learning approach, where the coefficient of determination R^2 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2012.02.154 | Expert Syst. Appl. |
Keywords | Field | DocType |
euclidean distance,mahalanobis distance,genetic algorithm,radial basis function,hybrid learning process,hybrid learning process method,redesigned radial basis function,hybrid learning approach,welding process,fitness function,determination r,radial basis,residual analysis | Radial basis function network,Radial basis function,Computer science,Evaluation function,Mahalanobis distance,Fitness function,Artificial intelligence,Covariance matrix,Artificial neural network,Machine learning,Covariance | Journal |
Volume | Issue | ISSN |
39 | 10 | 0957-4174 |
Citations | PageRank | References |
4 | 0.46 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rolando J. Praga-Alejo | 1 | 22 | 4.50 |
| Luis M. Torres-Treviño | 2 | 26 | 11.49 |
| David S. González-González | 3 | 8 | 1.89 |
| Jorge Acevedo-Dávila | 4 | 4 | 0.80 |
| Francisco Cepeda-Rodríguez | 5 | 4 | 0.46 |