A Neuroevolutionary Approach for System Identification-Reference-Cited by-同舟云学术

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A Neuroevolutionary Approach for System Identification

Published:2023-12-23 Issue:1 Volume:35 Page:64-73
ISSN:2195-3880
Container-title:Journal of Control, Automation and Electrical Systems
language:en
Short-container-title:J Control Autom Electr Syst

Author:

Carvalho Thiago^ORCID,Paiva Paulo,Vellasco Marley,Amaral José Franco,Coelho Pedro Henrique

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s40313-023-01061-x.pdf

Reference48 articles.

1. Aleksovski, D., Kocijan, J., & Džeroski, S. (2014). Model tree ensembles for the identification of multiple-output systems. In 2014 European control conference (ECC) (pp. 750–755). IEEE.

2. Aleksovski, D., Kocijan, J., & Džeroski, S. (2015). Ensembles of fuzzy linear model trees for the identification of multioutput systems. IEEE Transactions on Fuzzy Systems, 24(4), 916–929.

3. Billings, S. A. (2013). Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. John Wiley & Sons.

4. Bittanti, S., & Piroddi, L. (1997). Nonlinear identification and control of a heat exchanger: A neural network approach. Journal of the Franklin Institute, 334(1), 135–153.

5. Boccato, L., Lopes, A., Attux, R., & Von Zuben, F. J. (2011). An echo state network architecture based on volterra filtering and pca with application to the channel equalization problem. In The 2011 international joint conference on neural networks (pp. 580–587). IEEE.

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