Name
Abstract | ||
|---|---|---|
A Hybrid Learning Process method was fitted into a RBF. The resulting redesigned RBF intends to show how to test if the statistical assumptions are fulfilled and to apply statistical inference to the redesigned RBFNN bearing in mind that it allows to determine the relationship between a response (to a process) and one or more independent variables, testing how much each factor contributes to the total variation of the response is also feasible. The results show that statistical methods such as inference, Residual Analysis, and statistical metrics are all good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The foremost conclusion is that the resulting redesigned Radial Basis Function improved the accuracy of the model after using a Hybrid Learning Process; moreover, the new model also validates the statistical assumptions for using statistical inference and statistical analysis, satisfying the assumptions required for ANOVA to determine the statistical significance and the relationship between variables. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.engappai.2013.06.001 | Eng. Appl. of AI |
Keywords | Field | DocType |
new model,statistical inference,radial basis function neural,neural network model,hybrid learning process,statistical analysis,statistical assumption,hybrid learning process method,statistical metrics,statistical significance,statistical method,anova,radial basis function,residual analysis | Inference,Computer science,Fiducial inference,Statistical model,Statistical inference,Variables,Artificial intelligence,Statistical theory,Artificial neural network,Statistical assumption,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 8 | 0952-1976 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rolando J. Praga-Alejo | 1 | 22 | 4.50 |
| David S. González-González | 2 | 8 | 1.89 |
| Mario Cantú-Sifuentes | 3 | 10 | 2.29 |
| Pedro Perez-Villanueva | 4 | 2 | 1.10 |
| Luis M. Torres-Treviño | 5 | 26 | 11.49 |
| Bernardo D. Flores-Hermosillo | 6 | 4 | 0.76 |