Differential evolution trained radial basis function network: application to bankruptcy prediction in banks Online publication date: Fri, 07-May-2010
by Nekuri Naveen, V. Ravi, C. Raghavendra Rao, Nikunj Chauhan
International Journal of Bio-Inspired Computation (IJBIC), Vol. 2, No. 3/4, 2010
Abstract: In this paper, we propose differential evolution (DE) to train the supervised part of the radial basis function (RBF) network in the soft computing paradigm. Here the unsupervised part of the RBF is taken care of by K-means clustering. The new network is named as differential evolution trained radial basis function (DERBF) network. The efficacy of DERBF is tested on bank bankruptcy datasets viz. Spanish banks, Turkish banks, US banks and UK banks as well as benchmark datasets such as iris, wine and Wisconsin breast cancer. The performance of DERBF is compared with that of differential evolution trained wavelet neural networks (DEWNN) (Chauhan et al., 2009), threshold accepting trained wavelet neural network (TAWNN) (Vinaykumar et al., 2008) and wavelet neural network with respect to the criterion area under receiver operating characteristic curve. The results showed that DERBF is very good at generalisation in the ten-fold cross validation for all datasets.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook