There are many important fields involving the multilinear system identification. A great
number of parameters to be identified is an important challenge, leading to the need for …

This paper develops a hybrid remaining useful life (RUL) prediction method and explores
the feasibility for complex system equipment, using one of transmission equipment D-cables …

Tensor decompositions can be a powerful tool when faced with the curse of dimensionality
and have been applied in myriad applications. Their application to problems in the control …

In signal processing and data analytics, Wiener filter is a classical powerful tool to transform
an input signal to match a desired or target signal by a linear time-invariant (LTI) filter. The …

Kalman filter is one of the most important estimation algorithms, which estimates certain
unknown variables given the measurements observed over time subject to a dynamic …

This paper proposes an efficient global gravitational search (GGS) algorithm-assisted
Kalman filter (KF) design, called a GGS-KF technique, for accurate estimation of the Volterra …

Quantization is an important technique to transform the input sample values from a large set
(or a continuous range) into the output sample values in a small set (or a finite set). It has …

This paper aims at the parameter estimation of dual-rate Hammerstein-Volterra (DR-HV)
systems. The auxiliary model (AM) method is applied to solve the incomplete identification …

In recent years, the application of tensors has become more widespread in fields that involve
data analytics and numerical computation. Due to the explosive growth of data, low-rank …

The nonlinear system identification problem is solved for a multivariable nonlinear input–
output system that can be represented in terms of a Chen–Fliess functional expansion. The …