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Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G. and Ljung, L. (2014) Kernel Methods in System Identification, Machine Learning and Function Estimation: A Survey. Automatica, 50, 657-682.
https://doi.org/10.1016/j.automatica.2014.01.001

has been cited by the following article:

• TITLE: Geometrical Frameworks in Identification Problem

  AUTHORS: Nikolay Karabutov

  KEYWORDS: Framework, Nonlinear Dynamic System, Phase Portrait, Structural Identifi-cation, Nonlinearity, Structural Identifiability, Synchronizability, Lag, Lya-punov Exponent

  JOURNAL NAME: Intelligent Control and Automation, Vol.12 No.2, May 17, 2021

  ABSTRACT: The purpose of this review is to apply geometric frameworks in identification problems. In contrast to the qualitative theory of dynamical systems (DSQT), the chaos and catastrophes, researches on the application of geometric frameworks have not been performed in identification problems. The direct transfer of DSQT ideas is inefficient through the peculiarities of identification systems. In this paper, the attempt is made based on the latest researches in this field. A methodology for the synthesis of geometric frameworks (GF) is proposed, which reflects features of nonlinear systems. Methods based on GF analysis are developed for the decision-making on properties and structure of nonlinear systems. The problem solution of structural identifiability is obtained for nonlinear systems under uncertainty.