- Author
- Cepowski Tomasz (Maritime University of Szczecin)
- Title
- Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods
- Source
- Multidisciplinary Aspects of Production Engineering, 2018, vol. 1, s. 197-204, rys., tab., bibliogr. 11 poz.
- Keyword
- Regresja wielokrotna, Modele regresji, Sieci neuronowe
Multiple regression, Regression models, Neural networks - Note
- streszcz., summ.
- Abstract
- 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)
- Full text
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- Bibliography
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- Sigmund, S, el Moctar, O. (2018). Numerical and experimental investigation of added resistance of different ship types in short and long waves. OCEAN ENGINEERING, Volume: 147, pp.: 51-67.
- Watson, D.G.M. (1998). Practical Ship Design, Volume 1, Elsevier Science.
- Cited by
- ISSN
- 2545-2827
- Language
- eng
- URI / DOI
- http://dx.doi.org/10.2478/mape-2018-0026
