NARX Identification using Derivative-Based Regularized Neural Networks

TL;DR

This paper introduces a new regularization technique for NARX models that enhances stability and accuracy by penalizing input sensitivity, demonstrated through simulations showing improved performance over existing methods.

Contribution

The paper proposes a derivative-based regularization method for NARX neural networks, promoting exponential decay of input influence and improving model stability and accuracy.

Findings

Enhanced model stability and accuracy demonstrated in simulations

Outperforms existing regularization methods in simulation error

Promotes exponential decay of past input influence

Abstract

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.