Title :
A comparison of two neural network architectures for vector quantization
Author :
Naraghi-Pour, Mort ; Hedge, M. ; Bourge, Fabrice
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA, USA
Abstract :
The authors investigate the performance of two neural network architectures for vector quantization. The two architectures are the multilayer feedforward network and the Hopfield analog neural network. If is found that for the feedforward network to have reasonably good performance, the number of hidden units must be unrealistically high: exponential in the number of dimensions and codewords. For the Hopfield analog model, on the other hand, the number of processors required is equal to the number of codewords and the resulting performance is very close to the optimum mean squared error
Keywords :
data compression; encoding; neural nets; Hopfield analog neural network; codewords; dimensions; hidden units; multilayer feedforward network; neural network architectures; optimum mean squared error; performance; vector quantization; Computer architecture; Costs; Distortion measurement; Encoding; Feedforward neural networks; Hopfield neural networks; Image storage; Multi-layer neural network; Neural networks; Vector quantization;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155209
