Radial Basis Neural Network for Lossless Data Compression

The radial basis network is essentially a function approximator; article shows that this characteristic can be exploited for data compression applications. A variant of the radial basis network, the generalized regression neural network, is used in a two-stage compression scheme and its performance...

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Bibliographic Details
Published in:International journal of computers & applications 2002-01, Vol.24 (1), p.14-19
Main Authors: Logeswaran, R., Eswaran, C.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
data compression
Exact sciences and technology
Information, signal and communications theory
lossless compression
Miscellaneous
Neural networks
predictor
radial basis network
Signal processing
Telecommunications and information theory
two-stage compression
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Summary:The radial basis network is essentially a function approximator; article shows that this characteristic can be exploited for data compression applications. A variant of the radial basis network, the generalized regression neural network, is used in a two-stage compression scheme and its performance is evaluated in terms of the compression ratio. The training is imparted to the network using a block adaptive training method, and the trained network performs as a predictor-approximator in the first stage of compression. The optimum configuration of the network is arrived at by using a trial-and-error procedure. The compression ratios achieved by this network when used along with an arithmetic encoder in a two-stage compression scheme are obtained for different test files containing telemetry data. It is found that these results are comparable to those obtained with other known classical linear predictors.
ISSN:1206-212X
1925-7074
DOI:10.1080/1206212X.2002.11441655