Neural network dimension selection for dynamical system identification

Choosing an appropriate size of a network is an important issue for any neural network applications. The common practice is to start with an ldquoover-sizedrdquo network, then gradually reduces its size to find the optimal solution. In this paper, a new hybrid neural network pruning algorithm for multi-layer feedforward neural networks is investigated. Computer simulation results on system identification and pattern classification problems show this algorithm can significantly reduce the network dimension while still maintaining satisfactory identification and classification accuracy.