Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats
Само за регистроване кориснике
2014
Чланак у часопису (Објављена верзија)
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Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing st...able and accurate models. A detailed quantification of the differences between methods is provided.
Кључне речи:
Series 50 / resistance/trim evaluation / regression analysis / planing craft / hard chine hulls / artificial neural network (ANN)Извор:
Journal of Ship Production and Design, 2014, 30, 4, 153-174Издавач:
- Soc Naval Architects Marine Engineers, Jersey City
DOI: 10.5957/JSPD.30.4.140011
ISSN: 2158-2866
WoS: 000346037300001
Scopus: 2-s2.0-84908396094
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Radojčić, Dejan AU - Morabito, Michael G. AU - Simić, Aleksandar AU - Zgradić, Antonio B. PY - 2014 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1912 AB - Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided. PB - Soc Naval Architects Marine Engineers, Jersey City T2 - Journal of Ship Production and Design T1 - Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats EP - 174 IS - 4 SP - 153 VL - 30 DO - 10.5957/JSPD.30.4.140011 ER -
@article{ author = "Radojčić, Dejan and Morabito, Michael G. and Simić, Aleksandar and Zgradić, Antonio B.", year = "2014", abstract = "Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.", publisher = "Soc Naval Architects Marine Engineers, Jersey City", journal = "Journal of Ship Production and Design", title = "Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats", pages = "174-153", number = "4", volume = "30", doi = "10.5957/JSPD.30.4.140011" }
Radojčić, D., Morabito, M. G., Simić, A.,& Zgradić, A. B.. (2014). Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats. in Journal of Ship Production and Design Soc Naval Architects Marine Engineers, Jersey City., 30(4), 153-174. https://doi.org/10.5957/JSPD.30.4.140011
Radojčić D, Morabito MG, Simić A, Zgradić AB. Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats. in Journal of Ship Production and Design. 2014;30(4):153-174. doi:10.5957/JSPD.30.4.140011 .
Radojčić, Dejan , Morabito, Michael G., Simić, Aleksandar, Zgradić, Antonio B., "Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats" in Journal of Ship Production and Design, 30, no. 4 (2014):153-174, https://doi.org/10.5957/JSPD.30.4.140011 . .