Response analysis of feed-forward neural network predictors

We investigate the characteristics of some one-step-ahead nonlinear predictors based on a two-layer feed-forward neural network (2LFNN). The behavior of neural networks (NN) is investigated in the frequency domain using two frequency response estimation techniques, and in the time domain, by analyzing the unit step and triangular pulse responses. Some of the estimated frequency responses of these NNs resemble those of corresponding linear polynomial predictors, revealing the nearly polynomial nature of the applied training signals. The similarity of the two frequency response estimates is an indication of good generalization properties