This paper presents a variant of the generalized delta rule training algorithm for unsupervised learning in a single layer network using Gaussian function nonlinearities . An alternative to direct competition between the output nodes, that allows linear instead of quadratic complexity in the connections of the output nodes, is proposed. The connecting weights are adaptively modified with each presentation of an input pattern and converge towards values that are representative of the clustering structure of the input data. In order to calculate the weight increment, the training algorithm uses locally available information at the synapse and its connecting neuron. The proposed training algorithm, coupled with the alternative competition approach, eliminates the need for an orienting subsystem such as that needed in the ART1 model of Carpenter and Grossberg.
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