System Identification With General Dynamic Neural Networks and Network Pruning
This paper presents an exact pruning algorithm with adaptive pruning interval for General Dynamic Neural Networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg-Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible.