title: ;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.
authors.affiliation: Dept. of Phys., Illinois Univ., Urbana, IL.
authors.forename: T M
authors.initials: TM
authors.lastname: Martinetz
authors.forename: S G
authors.initials: SG
authors.lastname: Berkovich
authors.forename: K J
authors.initials: KJ
authors.lastname: Schulten