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

This tutorial review presents an overview of the achievements and some present research
activities in the field of state space based methods for nonlinear time-series analysis. In …

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

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

The present study proposes a multi-objective framework for structure selection of nonlinear
systems which are represented by polynomial NARX models. This framework integrates the …

It is suggested that the differences between the Hammerstein and Wiener models be
interpreted and understood in terms of the system eigenvalues. In particular, it is shown that …

This work focuses on obtaining models that may produce a better performance of Model
Predictive Controllers-MPC. Several papers published in the last 25 years have proposed …

Selecting the order of an input–output model of a dynamical system is a key step toward the
goal of system identification. The false nearest neighbors algorithm (FNN) is a useful tool for …

This text aims at providing a bird's eye view of system identification with special attention to
nonlinear systems. The driving force is to give a feeling for the philosophical problems facing …

Forecasting the solar cycle is of great importance for weather prediction and environmental
monitoring, and also constitutes a difficult scientific benchmark in nonlinear dynamical …