DocumentCode :
3327100
Title :
Multilayer perceptron and vector quantization
Author :
Xinwen, Wang ; Lihe, Zou ; Zhenya, He
Author_Institution :
DSP Lab., Southeast Univ., Jiangsu, China
fYear :
1991
fDate :
28 Oct-1 Nov 1991
Firstpage :
1361
Abstract :
The exponential encoding complexity has been the bottleneck drawback of vector quantization (VQ) in its applications. A kind of neural network multilayer perceptron (MLP) is introduced to attack the bottleneck. Based on the analysis of the VQ structure and the function of the MLP, an important conclusion on the relationship between the task and the scale required by it is drawn so that the two-layer MLP is adequate for VQ encoding or recognition. Simulation experiments are presented to test the theoretical analysis
Keywords :
encoding; neural nets; exponential encoding complexity; multilayer perceptron; neural network; vector quantization; Analytical models; Computational modeling; Encoding; Helium; Multi-layer neural network; Multilayer perceptrons; Neck; Neural networks; Pattern recognition; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
Type :
conf
DOI :
10.1109/IECON.1991.239070
Filename :
239070
Link To Document :
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