An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
On the application of orthogonal transformation for the design and analysis of feedforward networks
IEEE Transactions on Neural Networks
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The selection of an adequate hidden structure of a feedforward neural network is a very important issue of its design. When the hidden structure of the network is too large and complex for the model being developed, the network may tend to memorize input and output sets rather than learning relationships between them. In addition, training time will significantly increase when the network is unnecessarily large. We propose two methods to optimize the size of feedforward neural networks using orthogonal transformations. These two approaches avoid the retraining process of the reduced-size network, which is necessary in any pruning technique.