Research about the Layout of Canted Deck Based on Support Vector Machine

Article Preview

Abstract:

To determine the angle and position in width direction of canted deck objectively, this paper analyzed the factors that influence the angled deck general arrangements. Principal component analysis was introduced to determine the essential factors which influence the general arrangement of canted deck. Based on several flight deck schemes of foreign mature aircraft carriers, support vector machine was introduced to regression and gain the characteristics of canted deck arrangement. The training result gained by support vector machine algorithm is more accurate than other regression methods. The regression for former Soviet Union Ulyanovsk aircraft carrier demonstrates that the accuracy and validity of this method is good.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

11-14

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Sun Shinan. Modern Aircraft Carrier[M]. Shanghai: Shanghai Popular Science Press, (2000).

Google Scholar

[2] Xiangyin Meng. The Research on Some Key Problems in General Arrangement Design of Flight Deck [D]. Ph. D thesis. Harbin Engineering University, 2012.

Google Scholar

[3] Xiangyin Meng, Sheng Huang. A Method to design the Principal Dimensions of Large Vessels based on an Intuitionistic Fuzzy Hybrid Geometric Operator [J]. Journal of Harbin Engineering University, 2010, 31(8): 1000-1006.

Google Scholar

[4] Jinfeng Chen. Research and Application of Support Vector Regression [D]. Master Thesis. Jiangnan University, 2008, 5.

Google Scholar

[5] Wenlin Luo. On the Modeling of Ship Manoeuvring Motion by Using Support Vector Machines [D]. Ph. D thesis. Shanghai Jiao Tong University, 2009, 5.

Google Scholar

[6] Dongqin Li, Yifeng Guan, Linhai Kong. A new Weighted Support Vector Regression and its Application in Ship's Intelligent Modeling [J]. Journal of Jiangsu University of Science and Technology(Natural Science Edition), 2011. 25(2): 103-109.

Google Scholar

[7] Dongqin Li, Lizheng Wang, Chengfang Wang. Method of Support Vector Regression in Modeling Ship Principal Particulars[J]. Chinese Journal of Ship Research. 2007. 2(3): 18-39.

Google Scholar

[8] Jingxu Liu. Research on Model Selection for Support Vector Regression and Application of It[D]. Ph. D thesis. National University of Defense Technology, 2006, 4.

Google Scholar

[9] Shang Gao. Assessing the Effectiveness of Battleplane Based on Principal Component Analysis and Support Vector Machine[J]. Aeronautical Computer Technique. 2005, 35(3): 17-20.

Google Scholar