This paper reviews the major developments of modeling techniques applied to nonlinear
dynamics and chaos. Model representations, parameter estimation techniques, data …

Dataclusteringisacommontechniqueforstatis…, whichisusedin many? elds, including
machine learning, data mining, pattern recognition, image analysis and bioinformatics …

In this manuscript, system modeling and identification techniques are applied in developing
a prognostic yet deterministic model to forecast the spread of COVID-19 in India. The model …

Classical prediction error approaches for the identification of non-linear polynomial
NARX/NARMAX models often yield unsatisfactory results for long-range prediction or …

Linear-in-parameters models are quite widespread in process engineering, eg, nonlinear
additive autoregressive models, polynomial ARMA models, etc. This paper proposes a new …

Model structure selection plays a key role in non-linear system identification. The first step in
non-linear system identification is to determine which model terms should be included in the …

Este artigo tem dois objetivos principais. Em primeiro lugar, ele aborda a identificação de
sistemas nãolineares de forma introdutória, provendo literatura específica para um estudo …

An alternative solution to the model structure selection problem is introduced by conducting
a forward search through the many possible candidate model terms initially and then …

This paper investigates how the choice of the sampling time affects the identification of
nonlinear models. It is shown how the choice of the sampling interval can affect structure …

This paper is concerned with the estimation of dynamical invariants from relatively short and
possibly noisy sets of chaotic data. In order to overcome the difficulties associated with the …