Abstract
AbstractIn this paper, a node pruning algorithm based on optimal brain surgeon is proposed for feedforward neural networks. First, the neural network is trained to an acceptable solution using the standard training algorithm. After the training process, the orthogonal factorization is applied to the output of the nodes in the same hidden layer to identify and prune the dependant nodes. Then, a unit-based optimal brain surgeon(UB-OBS) pruning algorithm is proposed to prune the insensitive hidden units to further reduce the size of the neural network, and no retraining is needed. Simulations are presented to demonstrate the effectiveness of the proposed approach.
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