One of the major limitation of using the back-propagation neural network and its variants in real applications are that the number of hidden nodes is unknown. It is usually estimated by trial-and-error and thus it is inefficient. The paper proposes an algorithm to determine the number of hidden nodes based on the input data. The dynamic expansion and contraction approach (DECA), which comes from dynamic programming, is used to determine the optimal number of hidden nodes. The object function minimises the number of hidden nodes while the constraints are a pre-defined error. A short interval of train/test interleaving is used to minimise the learning time and avoid over-training the network. The algorithm is applicable to the neural network used for function approximation as well as pattern classification.
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