- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Dispersive neural networks for adaptive signal processing
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Dispersive neural networks for adaptive signal processing Day, Shawn P.
Abstract
Back-propagation is a popular method for training feed-forward neural networks. This thesis extends the back-propagation technique to dispersive networks, which contain internal delay elements. Both the delays and the weights adapt to minimize the error at the output. Dispersive networks can perform many tasks, including signal prediction, signal production, channel equalization, and spatio-temporal pattern recognition. For comparison with conventional techniques, a dispersive network was trained to predict future values of a chaotic signal using only its present value as an input. With adaptable delays, the network had less than half the prediction error of an identical network with fixed delays, and about one-quarter the error of a conventional back-propagation network. Moreover, a dispersive network can simultaneously adapt and predict, while a conventional network cannot. After training as a predictor, the network was placed in a signal production configuration, where it autonomously generated a close approximation to the training signal. The power spectrum of the network output was a good reproduction of the training signal spectrum. Networks with fixed time delays produced much less accurate power spectra, and conventional back-propagation networks were unstable, generating high-frequency oscillations. Dispersive networks also showed an improvement over conventional techniques in an adaptive channel equalization task, where the channel transfer function was nonlinear. The adaptable delays in the dispersive network allowed it to reach a lower error than other equalizers, including a conventional back-propagation network and an adaptive linear filter. However, the improved performance came at the expense of a longer training time. Dispersive networks can be implemented in serial or parallel form, using digital electronic circuitry. Unlike conventional back-propagation networks, they can operate in a fully pipelined fashion, leading to a higher signal throughput. Their implementation in analog hardware is a promising area for future research.
Item Metadata
Title |
Dispersive neural networks for adaptive signal processing
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
1993
|
Description |
Back-propagation is a popular method for training feed-forward neural networks. This thesis extends the back-propagation technique to dispersive networks, which contain internal delay elements. Both the delays and the weights adapt to minimize the error at the output. Dispersive networks can perform many tasks, including signal prediction, signal production, channel equalization, and spatio-temporal pattern recognition. For comparison with conventional techniques, a dispersive network was trained to predict future values of a chaotic signal using only its present value as an input. With adaptable delays, the network had less than half the prediction error of an identical network with fixed delays, and about one-quarter the error of a conventional back-propagation network. Moreover, a dispersive network can simultaneously adapt and predict, while a conventional network cannot. After training as a predictor, the network was placed in a signal production configuration, where it autonomously generated a close approximation to the training signal. The power spectrum of the network output was a good reproduction of the training signal spectrum. Networks with fixed time delays produced much less accurate power spectra, and conventional back-propagation networks were unstable, generating high-frequency oscillations. Dispersive networks also showed an improvement over conventional techniques in an adaptive channel equalization task, where the channel transfer function was nonlinear. The adaptable delays in the dispersive network allowed it to reach a lower error than other equalizers, including a conventional back-propagation network and an adaptive linear filter. However, the improved performance came at the expense of a longer training time. Dispersive networks can be implemented in serial or parallel form, using digital electronic circuitry. Unlike conventional back-propagation networks, they can operate in a fully pipelined fashion, leading to a higher signal throughput. Their implementation in analog hardware is a promising area for future research.
|
Extent |
6237316 bytes
|
Genre | |
Type | |
File Format |
application/pdf
|
Language |
eng
|
Date Available |
2008-09-18
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
|
DOI |
10.14288/1.0064917
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Graduation Date |
1993-05
|
Campus | |
Scholarly Level |
Graduate
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.