Time series modelling with recurrent CBP

We address the construction of recurrent neural networks by the use of constructive backpropagation (CBP). The benefits of the proposed scheme include: 1) fully recurrent networks with arbitrary number of layers can be constructed efficiently; and 2) after the network has been constructed one can continue the adaptation of the network weights as well as continue structure adaptation. This includes both addition and deletion of neurons/layers in a computationally efficient manner. Thus the investigated method is very flexible compared to many previous methods. In addition, according to our time series prediction experiments, the proposed method is competitive compared to the well known recurrent cascade-correlation method