Infrasound signal classification using parallel RBF Neural Networks
by Fredric M. Ham, Kamel Rekab, Ranjan Acharyya, Young-Chan Lee
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 1, No. 3/4, 2008

Abstract: A classification system is presented for discriminating different infrasound events using a Parallel Neural Network Classifier Bank (PNNCB) consisting of Radial Basis Function (RBF) networks. The classifier architecture and the pre-processing steps are unique and yield results that are superior when compared with those previously reported. Three-dimensional Receiver Operating Characteristic (ROC) curves are used to optimally set the output thresholds at each of the classification modules in the PNNCB for a particular class. A process is presented that enables optimising certain parameters of the classifier system. An application of the classification system to four infrasound classes is presented along with performance results and associated Confidence Intervals (CIs).

Online publication date: Fri, 26-Jun-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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