System identification is a mature research area with well established paradigms, mostly
based on classical statistical methods. Recently, there has been considerable interest in so …

There are two key issues for the kernel-based regularization method: one is how to design a
suitable kernel to embed in the kernel the prior knowledge of the LTI system to be identified …

Savitzky–Golay (SG) filtering is a classical signal smoothing technique based on the local
least squares approximation of the analyzed signal by a linear combination of known …

The kernel-based regularization method has two core issues: kernel design and
hyperparameter estimation. In this paper, we focus on the second issue and study the …

The aim of this article is to give a tutorial overview of frequency response function (FRF) or
impulse response (IR) function measurements of linear dynamic systems. These …

The notion of reproducing kernel Hilbert space (RKHS) has emerged in system identification
during the past decade. In the resulting framework, the impulse response estimation …

This work describes a novel nonparametric identification method for estimating impulse
responses of the general two-input single-output linear system with its target application to …

This paper presents an efficient nonparametric time domain nonlinear system identification
method. It is shown how truncated Volterra series models can be efficiently estimated …

Input design is an important issue for classical system identification methods but has not
been investigated for the kernel-based regularization method (KRM) until very recently. In …

This paper discusses the problem of system identification when frequency domain side-
information is available. We mainly consider the case where the side-information is provided …