Proposal of heuristic regression method applied in descriptive data analysis: case studies

• Autorzy: Gomes F. A. , Assis A. de O. , Reis M. R. da C. , Gomes V. M. , Oliveira S. G. M. , Araujo W. R. H. de , Calixto W. P.

Identyfikatory

DOI

10.22149/teee.v2i2.105

• Konferencja: International Conference on Environment and Electrical Engineering (17 ; 06-09.06.2017 ; Milan, Italy)
• Języki publikacji: EN

Abstrakty

EN

The purpose of this paper is to use the hybridized optimization method in order to find mathematical structures for analysis of experimental data. The heuristic optimization method will be hybridized with deterministic optimization method in order to that structures found require not knowledge about data generated experimentally. Five case studies are proposed and discussed to validate the results. The proposed method has viable solution for the analysis of experimental data and extrapolation, with mathematical expression reduced.

Słowa kluczowe

EN

regression; data analysis; optimization; heuristic; modeling

• Wydawca: EEEIC International Publishing
• Czasopismo: Transactions on Environment and Electrical Engineering
• Rocznik: 2017
• Tom: Vol. 2, No. 2
• Strony: 51--57
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Gomes F. A.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Assis A. de O.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Reis M. R. da C.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Gomes V. M.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Oliveira S. G. M.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Araujo W. R. H. de

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

autor

Calixto W. P.

• Federal Institute of Goiás and Federal, University of Goiás, Brazil

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).