Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

• Autorzy: Cepowski T.

Identyfikatory

DOI

10.2478/mape-2018-0026

• Języki publikacji: EN

Abstrakty

EN

The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

Słowa kluczowe

EN

identification; neural networks; regression; wave resistance

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2018
• Tom: Vol. 1, Iss. 1
• Strony: 197--204
• Opis fizyczny: Bibliogr. 11 poz., fig., tab.

Twórcy

autor

Cepowski T.

• Maritime University of Szczecin, Poland

Bibliografia

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• 11. Watson, D.G.M. (1998). Practical Ship Design, Volume 1, Elsevier Science.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).