The identification of affinely parameterized state–space system models is quite popular to
model practical physical systems or networked systems, and the traditional identification …

The precise calculation of solar irradiance is pivotal for forecasting the electric power
generated by PV plants. However, on-ground measurements are expensive and are …

In many practical applications of system identification, it is not feasible to measure both the
inputs applied to the system as well as the output. In such situations, it is desirable to …

We propose a new method for blind system identification (BSI). Resorting to a Gaussian
regression framework, we model the impulse response of the unknown linear system as a …

We consider the problem of estimating the occupancy level in buildings using indirect
information such as CO 2 concentrations and ventilation levels. We assume that one of the …

We present a new class of models, called uncertain-input models, that allows us to treat
system-identification problems in which a linear system is subject to a partially unknown …

Many classical problems in system identification, such as the classical prediction error
method and regularized system identification, identification of Hammerstein and cascaded …

Discrete-time linear time-varying (LTV) systems form a powerful class of models to
approximate complex dynamical systems with nonlinear dynamics for the purpose of …

Blind system identification is aimed at finding parameters of a system model when the input
is inaccessible. In this paper, we propose a blind system identification method that delivers a …

Abstract The Internet of Things (IoT) is a term that represents a huge technological trend that
is taking place: almost every device is being imbued with the intelligence of a …