Anzeige
Erschienen in:
2003 | OriginalPaper | Buchkapitel
Validation of a RBFN Model by Sensitivity Analysis
verfasst von : Özer Ciftcioglu
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Radial basis functions network (RBFN) is considered as a knowledge model. The model is established from a data set by learning. For the performance assessment a novel model validation method is introduced. The method consists of sensitivity analysis integrated into a mathematical-based technique known as analytical hierarchy process (AHP). It ranks the relative importance of factors being compared where the factors are the sensitivities in this case. The relative importance of the sensitivities is computed from the model and based on this information, the consistency of this information is tested by AHP. The degree of consistency is a measure of confidence for the validity of the model.