Baseline Results for Selected Nonlinear System Identification Benchmarks

TL;DR

This paper provides baseline results for five nonlinear system identification benchmarks, comparing ten methods to facilitate objective evaluation and promote discussion in the research community.

Contribution

It introduces a set of ten baseline techniques and their performance on five benchmarks to aid in fair comparison of nonlinear system identification methods.

Findings

Baseline methods establish reference performance levels.

Comparison highlights strengths and weaknesses of different approaches.

Results encourage objective evaluation in future research.

Abstract

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies.