Study on Hidden Layer Evaluation for Feed-Forward Neural Networks

The representation space and the target space, subspaces generated respectively by output vectors of hidden layer and expected output vectors, were analyzed. For hidden-layer-growing construction method, the error compensation performance of hidden units and the sufficient and necessary condition for the optimal performance of hidden units were investigated by calculating their error compensation values. The investigation shows that the evaluation factors for the hidden layer of a feed-forward neural network are composed of the dimensions of representation space and target space, the number of hidden units, and the error compensation efficiency of each hidden unit. Finally, the quality factor of hidden layer, the efficient coefficient of hidden layer, the redundancy of hidden units and the evaluation factor of hidden layer were defined, and the rationality and validity of the evaluation method were verified by reviewing some typical feedforward neural networks.