The nonlinear autoregressive exogenous (NARX) model shows a strong expression
capacity for nonlinear systems since these systems have limited information about their …

This paper focuses on the parameter estimation issues of multivariate equation‐error
autoregressive moving average systems. By applying the gradient search and the multi …

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 …

This paper introduces a hybrid Sage-Husa adaptive Kalman filter (SHAKF) with a
metaheuristic algorithm to estimate the parameters of the Hammerstein-Wiener (HW) model …

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 …

Block oriented models are recurrently employed to describe the dynamic characteristics of
nonlinear systems. This paper adopts an efficacious block-oriented model called cascaded …

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 …