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Validation of a RBFN Model by Sensitivity Analysis

  • Conference paper
Artificial Neural Nets and Genetic Algorithms
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Abstract

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.

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© 2003 Springer-Verlag Wien

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Ciftcioglu, Ö. (2003). Validation of a RBFN Model by Sensitivity Analysis. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_1

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  • DOI: https://doi.org/10.1007/978-3-7091-0646-4_1

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-00743-3

  • Online ISBN: 978-3-7091-0646-4

  • eBook Packages: Springer Book Archive

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