This paper develops and illustrates a new maximum-likelihood based method for the
identification of Hammerstein–Wiener model structures. A central aspect is that a very …

This paper discusses the importance and relevance of direct continuous-time system
identification and how this relates to the solution for model identification problems in …

Identification of time-continuous models from sampled data is a long standing topic of
discussion, and many approaches have been suggested. The Maximum Likelihood method …

Grey-box models are data-driven models where the structure is defined by the physics while
the parameters are calibrated using data. Low-order grey-box models of the building …

Analysis of directionally specific or causal interactions between regions in functional
magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with …

A grey-box model is a combination of data-driven and physics-based approaches to
modeling. For applications in buildings, grey-box models can be used as the control model …

In this paper, a novel framework to address the problem of parametric estimation for
continuous-time linear time-invariant dynamic systems is dealt with. The proposed …

Abstract Model predictive control (MPC) is a promising optimal control technique for
activating building energy flexibility using its thermal mass. The performance of the MPC …

The problem of estimating continuous-time model parameters of linear dynamical systems
using sampled time-domain input and output data has received considerable attention over …

In this paper we present a novel algorithm for identifying continuous-time autoregressive
moving-average models utilizing irregularly sampled data. The proposed algorithm is based …