Conference paper
A modified probabilistic neural network signal processor for nonlinear signals
Proceedings of 13th International Conference on Digital Signal Processing, Vol.1, pp.291-294
IEEE
13th International Conference on Digital Signal Processing Proceedings, DSP 97 (Santorini, Greece, 1997)
1997
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
Details
- Title
- A modified probabilistic neural network signal processor for nonlinear signals
- Authors/Creators
- A. Zaknich (Author/Creator) - The University of Western AustraliaY. Attikiouzel (Author/Creator) - Department of Physics, Mathematics and Informatics
- Publication Details
- Proceedings of 13th International Conference on Digital Signal Processing, Vol.1, pp.291-294
- Conference
- 13th International Conference on Digital Signal Processing Proceedings, DSP 97 (Santorini, Greece, 1997)
- Publisher
- IEEE
- Identifiers
- 991005541542007891
- Copyright
- © 1997 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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