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
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