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

• Autorzy: Tomasz Cepowski
• 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. (original abstract)

Słowa kluczowe

EN

Multiple regression; Regression models; Neural networks

PL

Regresja wielokrotna; Modele regresji; Sieci neuronowe

• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2018
• Tom: 1
• Strony: 197--204

Twórcy

autor

Tomasz Cepowski

• Maritime University of Szczecin

Bibliografia

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Identyfikatory

DOI

10.2478/mape-2018-0026