Nonlinear problems have drawn great interest and extensive attention from engineers,
physicists and mathematicians and many other scientists because most real systems are …

Utilizing meta-heuristic global optimization algorithms in gas turbine aero-engines modelling
and control problems is proposed over the past two decades as a methodological approach …

In this paper, a new Newton iterative identification method is presented for estimating the
parameters of a second-order dynamic system utilizing the obtained data from the step …

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 state-space identification for dynamical systems is most often performed by
minimizing the simulation error to reduce the effect of model errors. This optimization …

This paper focuses on the parameter and state estimation problems for observer canonical
state space systems from measurement information, derives a Kalman filter based least …

In this paper, fractional Hammerstein system identification is considered, where the linear
block is of fractional order. The original discrete Hammerstein system is first converted to a …

High-quality models are essential to the performance of many control-related tasks [1]-[3]. If
the structure of the system is known, first principle models can be created (which constitutes …

A new recursive algorithm is proposed for the identification of a special form of Hammerstein–
Wiener system with dead-zone nonlinearity input block. The direct motivation of this work is …

This paper derives a data filtering-based two-stage stochastic gradient algorithm and a data
filtering-based multistage recursive least-squares algorithm for input nonlinear output-error …