  This work presents a novel regularization method for the identification of
Nonlinear Autoregressive eXogenous (NARX) models. The regularization method
promotes the exponential decay of the influence of past input samples on the
current model output. This is done by penalizing the sensitivity of the NARX
model simulated output with respect to the past inputs. This promotes the
stability of the estimated models and improves the obtained model quality. The
effectiveness of the approach is demonstrated through a simulation example,
where a neural network NARX model is identified with this novel method.
Moreover, it is shown that the proposed regularization approach improves the
model accuracy in terms of simulation error performance compared to that of
other regularization methods and model classes.
