The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
'Neural-gas' network for vector quantization and its application to time-series prediction
Abstract
A neural network algorithm based on a soft-max adaptation rule is presented. This algorithm exhibits good performance in reaching the optimum minimization of a cost function for vector quantization data compression. The soft-max rule employed is an extension of the standard K-means clustering procedure and takes into account a neighborhood ranking of the reference (weight) vectors. It is shown that the dynamics of the reference (weight) vectors during the input-driven adaptation procedure are determined by the gradient of an energy function whose shape can be modulated through a neighborhood determining parameter and resemble the dynamics of Brownian particles moving in a potential determined by the data point density. The network is used to represent the attractor of the Mackey-Glass equation and to predict the Mackey-Glass time series, with additional local linear mappings for generating output values. The results obtained for the time-series prediction compare favorably with the results achieved by backpropagation and radial basis function networks.
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Elements of Artificial Neural Networks
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Metadata
- Authors
- Martinetz, T. M., Berkovich, S. G., & Schulten, K. J.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1993-07
- Volume
- 4
- Issue
- 4
- Pages
- 558-569
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2166322089
- Cited by
- 1,431
- References
- 31
- FWCI
- 15.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Martinetz, T. M., Berkovich, S. G., & Schulten, K. J. (1993). 'Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.238311
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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