We review the problem of estimating parameters and unobserved trajectory components
from noisy time series measurements of continuous nonlinear dynamical systems. It is first …

The paper gives a survey of errors-in-variables methods in system identification.
Background and motivation are given, and examples illustrate why the identification problem …

The traditional paradigm of system identification employs prior information on system
structures and environments and input/output observation data to derive system models …

In this article the author describes the development of system identification in the control
community as he has observed it over the last 30 years, both as a student of the subject …

In (operational) modal analysis the modal parameters of a structure are identified from the
response of that structure to (unmeasurable operational) perturbations. A key issue that …

This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

This paper proposes a stability-guaranteed bias-compensated normalized least-mean-
square (BC-NLMS) algorithm for noisy inputs. The bias-compensated algorithms require the …

This paper deals with the identification of errors-in-variables (EIV) models corrupted by
additive and uncorrelated white Gaussian noises when the noise-free input is an arbitrary …

Discrete-Time Linear Systems: Theory and Design with Applications combines system
theory and design in order to show the importance of system theory and its role in system …

Identification problems of output error models with moving average noises are considered in
this paper. The least‐squares‐based parameter estimation is biased under the colored …