Automatic Fusion and Splitting of Artificial Neural Elements in Optimizing the Network Size
نویسندگان
چکیده
– A three-layered neural network that optimally self-adjusts the number of hidden layer units is proposed. The network combines two techniques : 1) Unit fusion which enables an efficient pruning of the redundant units. 2) Linear transformations applied to the chosen hidden layer unit pair output and a modified back-propagation training rule for gradual fusion to reduce pruning shocks. The network was applied to a character recognition problem and it adjusted itself to a minimal configuration at high rate.
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