NARX Identification using Derivative-Based Regularized Neural Networks
This work addresses model stability and accuracy for NARX identification in control or system modeling domains, but it appears incremental as it builds on existing regularization techniques.
The paper tackles the problem of identifying Nonlinear Autoregressive eXogenous (NARX) models by proposing a novel regularization method that penalizes sensitivity to past inputs to promote stability and improve model quality. The result is improved model accuracy in simulation error performance compared to other regularization methods and model classes, as demonstrated in a simulation example.
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.