Browse > Article
http://dx.doi.org/10.5574/KSOE.2015.29.2.111

Practical Application of Neural Networks for Prediction of Ship's Performance Factors  

Kim, Hyun-Cheol (school of Mechanical Engineering, Ulsan College)
Park, Hyoung-Gil (Samsung Ship Model Basin, Central Research Institute, Samsung Heavy Industries Co., Ltd.)
Publication Information
Journal of Ocean Engineering and Technology / v.29, no.2, 2015 , pp. 111-119 More about this Journal
Abstract
In the initial ship design stage, performance predictions are generally carried out before and after the hull form design. The former is based on the main dimensions and power information, and the latter is based on the geometry of the hull form and propeller. This paper deals with the practical application of neural networks for the prediction of a ship's performance factors before and after the hull form design. For this, the hull form parameters that affect the performance are studied, and an optimal neural network structure based on the SSMB database is constructed. By comparing the results predicted by neural networks and the model test results, we confirmed that neural networks can be applied to practically evaluate the performance in the initial ship design stage.
Keywords
Neural Networks; Performance factors; Resistance factors; Self-propulsion factors;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Holtrop, J., Mennen, G.G.J., 1982. An Approximate Power Prediction Method. International Shipbuilding Progress, 29.
2 Kanai, T., 2000. Application of the Neural Network to Estimate of Ship's Propulsive Performance and Hull Form Optimization(in Japan). Transactions of The West-Japan Society of Naval Architects, 99, 1-11.
3 Kim, S.Y., Kim, H.C., Lee, K.H., Kim, J.N., Son, Y.D., 1995. Determination of Hull form Factors about a High Speed Coastal Fishing Boat using Fuzzy Modeling. Journal of the Society of Naval Architects of Korea, 32(4), 1-8.
4 Kim, S.Y., Kim, H.C., 1998. A Development of Neuofuzzy System for a Conceptual Design of Ship. Journal of the Society of Naval Architects of Korea, 35(3), 79-87.
5 Kim, S.Y., Kim, H.C., Lee, C.R., 1998a. The Development of Initial Main Particulars and a Hull Form Generation Using a Neurofuzzy Modeling. Journal of Ocean Engineering and Technology, 12(3), 103-111.
6 Kim, S.Y., Kim, H.C., Yeo, K.H., Kim, M.J., 1998b. Generation of Sectional Area Curve using an ANFIS and a B-Spline Curve. Journal of Ocean Engineering and Technology, 12(3), 96-102.
7 Kim, H.C., Lee, K.S., Kim, S.Y., 1997. Generation of SAC using a ASMOD and a Hybrid Curve Approximation. Journal of The Korean Institute of Intelligent Systems, 11(7), 435-438.
8 Kim, S.Y., Lee, Y.S., 1992. Preliminary Hull Form Generation Using Fuzzy Model. Journal of the Society of Naval Architects of Korea, 29(4), 36-44.
9 Kim, S.Y., Shin, S.C., Gim, T.G., 2002. Auto Classification of Ship Surface Plates by Neural Networks. Journal of The Korean Institute of Intelligent Systems, 12(2), 103-108.   DOI
10 Minia, A.A., Williams, R.D., 1990. Acceleration of Back-Propagation through Learning Rate and Momentum Adaption. International Joint Conference on Neural Networks, 1, 676-679.
11 Neuralware, 2001. Neural Computing, A Technology Handbook for NeuralWorks Professional II/PLUS.
12 Rummelhart, D.E., Hinton, G.E., William, R., 1985. Learning Representations by Error Propagation. Institute for Cognitive Science Report 8506, San Diego, University of California.
13 Shin, S.C., Kim, S.Y., Park, J.K., 2002. Evaluation of Engine Room Machinery Arrangement using Fuzzy Modeling. Journal of The Korean Institute of Intelligent Systems, 12(2), 157-163.   DOI
14 Shin, S.C., Bae, J.H., Kim, H.S., Kim, S.H., Kim, S.Y., Lee, J.K, 2012. Estimation of Environmental Costs Based on Size of Oil Tanker Involved in Accident using Neural Networks. Journal of Ocean Engineering and Technology, 26(1), 60-63.   DOI
15 Son, H.J., Kim, H.C., 2008. Remodeling of Hull Form and Calculation of Design Parameters using Cubic Composite Spline. Transactions of the Society of CAD/CAM Engineers, 13(6), 440-449.