Title :
Results of data compression for plane curves using neural networks
Author :
Regattieri, M. ; Netto, M. Andrade ; Rocha, A.F.
Author_Institution :
CEFET, Curitiba, Brazil
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper provides some results of comparing two neural net structures´ performances in compressing input patterns from a bidimensional original curve. We apply an interpolation algorithm based on nonlinear fuzzy rules to regenerate the compressed information. Resulting interpolation errors and compression capacity are features that are analysed. As an illustration we apply the compression system and interpolation method to some curves, and show identical and different results for two models-symbolic and numerical. In terms of abstraction capacity, better results were obtained for symbolic processing model. The excellent results demonstrate the capability of compression system in extracting the minimal necessary information
Keywords :
data compression; fuzzy neural nets; interpolation; abstraction capacity; compressed information regeneration; compression capacity; data compression; interpolation; neural networks; nonlinear fuzzy rules; numerical models; plane curve compression; symbolic models; Backpropagation algorithms; Biological neural networks; Data compression; Data mining; Fuzzy neural networks; Information systems; Interpolation; Neural networks; Numerical models; Power system modeling;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374411
