We review the development and extensions of the classical total least-squares method and
describe algorithms for its generalization to weighted and structured approximation …

Principal component analysis (PCA) has been widely used for monitoring complex industrial
processes with multiple variables and diagnosing process and sensor faults. The objective …

The behavioral approach, put forward in the three part paper by JC Willems [Wil87], includes
a rigorous framework for deriving mathematical models, a field called system identification …

In this paper, we make a comparison between dynamic principal component analysis (PCA)
and errors-in-variables (EIV) subspace model identification (SMI) and establish consistency …

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 …

Most subspace identification algorithms are not applicable to closed-loop identification
because they require future input to be uncorrelated with past innovation. In this paper, we …

The following identification problem is considered: Minimize the/spl lscr//sub 2/norm of the
difference between a given time series and an approximating one under the constraint that …

Takagi–Sugeno fuzzy models have proved to be a powerful tool for the identification of
nonlinear dynamic systems. Their generic nonlinear model representation is particularly …

The paper is about a generalization of a classical eigenvalue-decomposition method
originally developed for errors-in-variables linear system identification to handle an …

A standard assumption for consistent estimation in the errors-in-variables setting is
persistency of excitation of the noise-free input signal. We relax this assumption by …