Nonlinear system modeling based on constrained Volterra series estimates

TL;DR

This paper introduces a Volterra series-based nonlinear system modeling method using constrained least squares, effective with limited data and prior knowledge, and compares different regularization parameters on benchmark systems.

Contribution

It proposes a novel modeling algorithm employing $l_q$-constrained least squares for nonlinear systems with limited data, providing theoretical guarantees and empirical validation.

Findings

Models for $q>1$ better capture system characteristics.

Sparse solutions for $q=1$ achieve lower input-output error.

Performance guarantees hold even with model complexity exceeding data points.

Abstract

A simple nonlinear system modeling algorithm designed to work with limited \emph{a priori }knowledge and short data records, is examined. It creates an empirical Volterra series-based model of a system using an $l_{q}$-constrained least squares algorithm with $q\geq 1$. If the system $m\left( \cdot \right) $ is a continuous and bounded map with a finite memory no longer than some known $\tau$, then (for a $D$ parameter model and for a number of measurements $N$) the difference between the resulting model of the system and the best possible theoretical one is guaranteed to be of order $\sqrt{N^{-1}\ln D}$, even for $D\geq N$. The performance of models obtained for $q=1,1.5$ and $2$ is tested on the Wiener-Hammerstein benchmark system. The results suggest that the models obtained for $q>1$ are better suited to characterize the nature of the system, while the sparse solutions obtained for $q=1$ yield smaller error values in terms of input-output behavior.