Fast adaptive IIR-MLP neural networks for signal processing applications

Neural networks with internal temporal dynamic can be applied to non-linear DSP problems. The classical fully connected recurrent architectures, can be replaced by less complex neural networks, based on the well known MultiLayer Perceptron (MLP) where the temporal dynamic is modelled by replacing each synapses either with a FIR filter or with an IIR filter. A general learning algorithm (Back-PropagationThrough-Time or BPTT) for a dynamic neural MLP has been introduced by P.J. Werbos. This is a non-causal algorithm, being able to work only in batch mode, while many real problems require on-line adaptation. In this paper we show a new on-line learning algorithm for the IIR-MLP networks which is an approximation of the true BPTT and which includes, as a particular case, the already known Back and Tsoi algorithm. Several computer simulations of identification of dynamical systems are also reported.