Operational modal analysis deals with the estimation of modal parameters from vibration
data obtained in operational rather than laboratory conditions. This paper extensively …

Process monitoring systems are necessary for ensuring the long-term reliability of the
operation of industrial systems. This article provides some perspectives on progress in the …

Part I deals with the mathematical preliminaries: numerical linear algebra; system theory;
stochastic processes; and Kalman filtering. Part II explains realization theory as applied to …

In this paper we describe two algorithms to identify a linear, time-invariant, finite dimensional
state space model from input-output data. The system to be identified is assumed to be …

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 analyze a simple prefiltered variation of the least squares estimator for the problem of
estimation with biased,\emph {semi-parametric} noise, an error model studied more broadly …

Real-world sequential decision making problems commonly involve partial observability,
which requires the agent to maintain a memory of history in order to infer the latent states …

Two identification algorithms, an iterative least-squares and a recursive least-squares, are
developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear …

It is well-known that the stochastic gradient (SG) identification algorithm has poor
convergence rate. In order to improve the convergence rate, we extend the SG algorithm …

In this paper, we use a hierarchical identification principle to study identification problems for
multivariable discrete-time systems. We propose a hierarchical gradient iterative algorithm …