Identification of Nonlinear Systems Using Parallel Laguerre-NN Model ������ ����

In this paper, a nonlinear system identification framework using parallel linear-plus-neural
networks model is developed. The framework is established by combining a linear Laguerre filter
model and a nonlinear neural networks (NN) model in a parallel structure. The main advantage of the
proposed parallel model is that by having a linear model as the backbone of the overall structure,
reasonable models will always be obtained. In addition, such structure provides great potential for
further study on extrapolation benefits and control. Similar performance of proposed method with
other conventional nonlinear models has been observed and reported, indicating the effectiveness of
the proposed model in identifying nonlinear systems.