Nonlinear system identification using integrated linear-NN models: series vs. parallel structures

In this paper, the performance of integrated linear-NN models is investigated for nonlinear
system identification using two different structures: series vs. parallel. In particular, Laguerre filters are
selected as the linear models, and multi-layer perceptron (MLP) or feed-forward neural networks (NN) are
selected for the nonlinear models. Results show promising capability of the (novel) parallel Laguerre-NN
structure especially in terms of its generalization capability when subjected to data different from those used
during the identification stage in comparison to the series Laguerre-NN.