The identification of linear parameter-varying systems in an input–output setting is
investigated, focusing on the case when the noise part of the data generating system is an …

Over the last decade, smart sensors have grown in complexity and can now handle multiple
measurement sources. This work establishes a methodology to achieve better estimates of …

In this study, a data-driven technique is proposed to deal with multivariable fixed-order
controller design. The method is based on the virtual reference feedback tuning (VRFT) …

This study applies the filtering technique to system identification to study the data filtering‐
based parameter estimation methods for multivariable systems, which are corrupted by …

This brief presents a new framework for the identification of nonlinear autoregressive (AR)
models with exogenous inputs (NARX) model for design (NARX-M-for-D), which represents …

We consider large scale covariance estimation using a small number of samples in
applications where there is a natural ordering between the random variables. The two …

Nonlinear grey system models, serving to time series forecasting, are extensively used in
diverse areas of science and engineering. However, most research concerns improving …

Dynamical test benches are typically used in the development phase of engine systems and
require tracking controllers with a high performance. Unfortunately, during such a work the …

The present work deals with the identification of multivariable linear dynamic system from
noisy input–output observations, where the input signal is arbitrary and the input–output …

In this paper, we propose a two-stage algorithm utilizing the Cauchy integral and the matrix-
valued adaptive Fourier decomposition (abbreviated as matrix AFD) to identify transfer …