We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A Study on Regression Analysis by Expanded RBF Network Based on Copula with Linear Correlation and Rank Correlation.
- Authors
Ota, Shuhei; Kimura, Mitsuhiro
- Abstract
We extend the traditional RBF network to be a more powerful tool in terms of considering dependence among explanatory variables. For this purpose, we propose two kernel functions of RBF network, i.e., FGM-Gauss kernel and ρ-Gauss kernel based on a copula. A copula is another expression of a joint probability distribution function. After proposing the new models, we compare the regression performances between RBF network with the traditional Gauss kernel, FGM-Gauss kernel, ρ-Gauss kernel, and the multiple linear regression analysis by numerical experimentations. We show that new models have better regression performances than RBF network with Gauss kernel and multiple regression analysis if the explanatory variables depend on each other.
- Subjects
RADIAL basis functions; REGRESSION analysis; RANK correlation (Statistics); COPULA functions; DISTRIBUTION (Probability theory)
- Publication
International Journal of Reliability, Quality & Safety Engineering, 2015, Vol 22, Issue 5, p-1
- ISSN
0218-5393
- Publication type
Article
- DOI
10.1142/S0218539315500229