Title
A comparison of two neural network architectures for vector quantization
Document Type
Conference Proceeding
Publication Date
12-1-1991
Abstract
The authors investigate the performance of two neural network architectures for vector quantization (VQ). The two architectures are the multilayer feedforward network and the Hopfield analog neural network. It 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.
Publication Source (Journal or Book title)
Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks
First Page
391
Last Page
396
Recommended Citation
Naraghi-Pour, M., Hegde, M., & Bourge, F. (1991). A comparison of two neural network architectures for vector quantization. Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks, 391-396. Retrieved from https://repository.lsu.edu/eecs_pubs/1092