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`Neural-gas' network for vector quantization and its application to time-series prediction

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3 Author(s)
T. M. Martinetz ; Dept. of Phys., Illinois Univ., Urbana, IL, USA ; S. G. Berkovich ; K. J. Schulten

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

Published in:

IEEE Transactions on Neural Networks  (Volume:4 ,  Issue: 4 )