System identification problems are always challenging to address in applications that
involve long impulse responses, especially in the framework of multichannel systems. In this …

In applications involving system identification problems, some characteristics of the impulse
response of the system to be identified are usually exploited to design adaptive algorithms …

In linear system identification problems, it is important to reveal and exploit any specific
intrinsic characteristic of the impulse responses, in order to improve the overall performance …

We derive a set of causal deep neural networks whose architectures are a consequence of
tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural …

System identification problems can be efficiently addressed by exploiting some specific
characteristics of the impulse responses. In this paper, we focus on the identification of …

The Kalman filter represents a very popular signal processing tool, with a wide range of
applications within many fields. Following a Bayesian framework, the Kalman filter …

Tensor Train (TT) is a tensor decomposition technique designed to resolve the curse of
dimensionality and the intermediate memory blow-up problems in traditional techniques for …

We propose a nonnegative tensor decomposition with focusing on the relationship between
the modes of tensors. Traditional decomposition methods assume low-rankness in the …

The affine projection algorithm (APA) represents a popular choice in system identification
scenarios, especially with correlated input signals. In this paper, we address the multilinear …

Nonlinear systems have been studied for a long time and have applications in numerous
research fields. However, there is currently no global solution for nonlinear system …