Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

In this study, the authors present an overview of closed‐loop subspace identification
methods found in the recent literature. Since a significant number of algorithms has …

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

The original edition of this book was published in 1988. The first author, Ted Hannan,
passed away in 1994. Since the book went out of print a decade ago, I have been …

In this paper, we analyze the finite sample complexity of stochastic system identification
using modern tools from machine learning and statistics. An unknown discrete-time linear …

This book is intended primarily as a handbook for engineers who must design practical
systems. Its primary goal is to discuss model development in sufficient detail so that the …

In this article, we study the design of controllers in the context of stochastic optimal control
under the assumption that the model of the system is not available. This is, we aim to control …

This book is intended to be a treatise on the theory and modeling of secondorder stationary
processes with an exposition of some application areas which we believe are important in …

This paper examines the problem of estimating linear time-invariant state-space system
models. In particular, it addresses the parametrization and numerical robustness concerns …

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