Neural networks applied to signal processing

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Authors
Baehre, Mark D.
Subjects
Neural network
backpropagation
conjugate gradient method
Fibonacci line search
nonlinear signal processing
channel equalization
artificial intelligence
computer architecture
Advisors
Tummala, Murali
Date of Issue
1989-09
Date
September 1989
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
The relationship between the structure of a neural network and its ability to perform nonlinear mapping is analyzed. A new algorithm, called the conjugate gradient optimization method, for calculating the weights and thresholds of a neural network is presented. The performance of the conjugate gradient algorithm is then compared to the well known backpropagation method and shown to be more computationally efficient. A neural network using the conjugate gradient algorithm is then applied to three simple examples to demonstrate its signal processing capabilities. The first example illustrates the ability of the neural network to perform classification. The second compares the performance of a one-step linear predictor to a neural network for a nonlinear chaotic time series. The neural network predictor is shown to provide much greater accuracy than its linear counterpart. The final application presented demonstrates the ability of a neural network to perform channel equalization for a nonmininmum phase channel. Its performance is then compared to its linear equivalent.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
96 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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