In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

Subspace Identification for Linear Systems focuses on the theory, implementation and
applications of subspace identification algorithms for linear time-invariant finite-dimensional …

Subspace-based methods for system identification have attracted much attention during the
past few years. This interest is due to the ability of providing accurate state-space models for …

We give a general overview of the state-of-the-art in subspace system identification
methods. We have restricted ourselves to the most important ideas and developments since …

Many of the existing offline system identification methods cannot completely comprehend
the dynamics of an evolving complex process without relying on impractically large data …

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

We give a general overview of the state of the art in subspace system identification methods.
We have restricted ourselves to the most important ideas and developments since the …

Complex function theory and linear algebra provide much of the basic mathematics needed
by engineers engaged in numerical computations, signal processing or control. The transfer …

In this paper we present a new subspace identification algorithm for the identification of multi-
input multi-output linear time-invariant continuous-time systems from measured frequency …

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 …