Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

This article focuses on the parameter estimation problem of the input nonlinear system
where an input variable‐gain nonlinear block is followed by a linear controlled …

The Volterra model can represent a wide range of nonlinear dynamical systems. However,
its practical use in nonlinear system identification is limited due to the exponentially growing …

Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

Nonlinear problems have drawn the attention of many researchers, engineers and scientists
as most of the real systems are inherently nonlinear in nature. These systems widely exist in …

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 …

The Volterra series consists of a powerful method for the identification of non-linear
relationships. However, the identification of the series active basis sets requires intense …

This paper presents the handling of nonlinear system identification problem based on
Volterra-type nonlinear systems. An efficient arithmetic optimization algorithm (AOA) along …

Nonlinear dynamic systems are often approximated by a Volterra series, which is a
generalization of the Taylor series for systems with memory. However, the Volterra series …

An online modeling algorithm is derived from a generic stochastic dual averaging (DA)
method. It employs a negative entropy as a distance-generating function and the Volterra …