A modified probabilistic neural network signal processor for nonlinear signals

Zaknich, A. and Attikiouzel, Y. (1997) A modified probabilistic neural network signal processor for nonlinear signals. In: 13th International Conference on Digital Signal Processing Proceedings, DSP 97, 291 - 294, Santorini, Greece pp. 291-294.

Abstract

This paper introduces a practical and very effective network for nonlinear signal processing called the modified probabilistic neural network. It is a regression technique which uses a single radial basis function kernel whose bandwidth is related to the noise statistics. It has special advantages in application to time and spatial series signal processing problems because it is constructed directly and simply for the training signal waveform features. A sonar signal processing problem is used to illustrate its operation and to compare it with some other filters and neural networks

Publication Type:	Conference Paper
Publisher:	IEEE
Copyright:	© 1997 IEEE
URI:	http://researchrepository.murdoch.edu.au/id/eprint/18522

Item Control Page

Downloads