Pruning of recurrent neural models: an optimal brain damage approach

This paper considers the problem of pruning recurrent neural models of perceptron type with one hidden layer which may be used for modelling of dynamic system. In order to reduce the number of model parameters (i.e. the number of weights), the Optimal Brain Damage (OBD) pruning algorithm is adopted for the recurrent neural models. Efficiency of the OBD algorithm is demonstrated for pruning neural models of a neutralisation reactor benchmark process. For the considered neutralisation system, the OBD algorithm makes it possible to reduce as many as 60% of model parameters and reduce the validation error by some 30% when compared to the full (unpruned) models.