A new approach to the identification of linear repetitive processes based on subspace
algorithms is presented. The order of a linear repetitive process, the unknown process …

The aim of this paper is to propose a new approach to the identification of the LRPs based
on subspace algorithms. The order of a LRP and the unknown process matrices are …

This paper presents a new parallel approach to identification of linear repetitive processes
based on subspace algorithms. Parallel realizations of these algorithms are tested on …

Recursive subspace identification methods have been an active area of research in recent
years. In this paper, a recursive version of a closed-loop subspace method is proposed for …

A recent identification study using the Tennessee Eastman Process model revealed that the
MOESP-based (multiple-input multiple-output output error state space) algorithm (ie …

This work analyses a recently proposed statistically based technique for monitoring complex
dynamic process systems [17]. The technique utilises a state space model that is cast into …

This paper presents the 2-stage identification approach. The first stage is the identification of
the system step response from the experimental data using the frequency sampling filter …

In this paper, an approach to recursive subspace identification based on the coordinate-free
framework of subspace identification is proposed. Herein, the predictor space serves as a …

In this paper we propose a new nonparametric approach to identification of linear time
invariant systems using subspace methods. The nonparametric paradigm to prediction of …

A new three step closed loop subspace identifications algorithm based on an already
existing algorithm and the Kalman filter properties is presented. The Kalman filter contains …