Abstract
A hybrid GrayART network consisting of an unsupervised clustering stage and a supervised adjusting stage is proposed in this paper. In this hybrid learning scheme, the unsupervised clustering stage estimates cluster centers of a given dataset, and the supervised adjusting stage follows to tune the cluster centers for improving the training result. With a proper vigilance threshold value, the overall learning process needs only two epochs, one for each stage. The hybrid network is applied to solve the Iris dataset for illustration. Simulation results demonstrate the classification ability and effectiveness of the proposed network.
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