COVID-19 has disrupted normal life and has enforced a substantial change in the policies,
priorities and activities of individuals, organisations and governments. These changes are …

This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

In control and monitoring of manufacturing processes, it is key to understand model
uncertainty in order to achieve the required levels of consistency, quality, and economy …

In this paper, a novel recursive learning identification approach is proposed to estimate the
parameters of the Wiener systems with quantized output. By using a filter with adaptive …

The problem of state estimation of a linear, dynamical state-space system where the output
is subject to quantization is challenging and important in different areas of research, such as …

This article studies how to estimate the weighted adjacency matrix of a network out of the
state sequence of a model with binary-valued states, by using a recursive algorithm. In the …

In this paper we develop two filtering algorithms for state-space systems with binary outputs.
We approximate the conditional probability mass function of the output signal given the state …

For identifying the parameters in finite impulse response (FIR) systems via the quantized
output, existing expectation maximization (EM) methods are involved with the intractable …

Sampling in control applications is increasingly done non-equidistantly in time. This includes
applications in motion control, networked control, resource-aware control, and event-based …

The paper gives a survey of errors-in-variables methods in system identification.
Background and motivation are given, and examples illustrate why the identification problem …