Statistical inference in a redesigned Radial Basis Function neural network

Authors:
Rolando J. Praga-Alejo;David S. GonzáLez-GonzáLez;Mario Cantú-Sifuentes;Pedro Perez-Villanueva;Luis M. Torres-TreviñO;Bernardo D. Flores-Hermosillo
Affiliations:
-;-;-;-;-;-
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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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.