선박의 성능 요소 추정을 위한 신경망의 실용화 연구
Practical Application of Neural Networks for Prediction of Ship’s Performance Factors
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초록
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.
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.
참고문헌 (15)
- Holtrop, J., "An Approximate Power Prediction Method", International Shipbuilding Progress, pp.29~ (1982)
- Kanai, T., "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, pp.1~11 (2000)
- Kim, S. Y., "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), pp.1~8 (1995)
- Kim, S. Y., "A Development of Neuofuzzy System for a Conceptual Design of Ship", Journal of the Society of Naval Architects of Korea, 35(3), pp.79~87 (1998)
- Kim, S. Y., "The Development of Initial Main Particulars and a Hull Form Generation Using a Neurofuzzy Modeling", Journal of Ocean Engineering and Technology, 12(3), pp.103~111 (1998)
- Kim, S. Y., "Generation of Sectional Area Curve using an ANFIS and a B-Spline Curve", Journal of Ocean Engineering and Technology, 12(3), pp.96~102 (1998)
- Kim, H. C., "Generation of SAC using a ASMOD and a Hybrid Curve Approximation", Journal of The Korean Institute of Intelligent Systems, 11(7), pp.435~438 (1997)
- Kim, S. Y., "Preliminary Hull Form Generation Using Fuzzy Model", Journal of the Society of Naval Architects of Korea, 29(4), pp.36~44 (1992)
- 김수영, "신경망을 이용한 선박의 곡가공 외판 분류 자동화", 한국지능시스템학회 논문지, 12(2), pp.103~108 (2002), 한국지능시스템학회
- Minia, A. A., "Acceleration of Back-Propagation through Learning Rate and Momentum Adaption", International Joint Conference on Neural Networks, 1, pp.676~679 (1990)
- PLUS, "A Technology Handbook for NeuralWorks Professional II", pp.2001, Neuralware
- Learning Representations by Error Propagation, "Rummelhart, D. E.", University of California, ~, pp.Institute for Cognitive Science Report 8506 (1985)
- 신성철, "퍼지모델링을 이용한 기관실 장비 배치 평가", 한국지능시스템학회 논문지, 12(2), pp.157~163 (2002), 한국지능시스템학회
- 신성철, "신경망을 이용한 유조선 기름 유출사고에 따른 환경비용 추정에 관한 연구", 한국해양공학회지, 26(1), pp.60~63 (2012), 한국해양공학회
- 손혜종, "3차 복합 스플라인을 이용한 선형의 리모델링 및 설계 파라메터 계산", 한국CDE학회 논문집, 13(6), pp.440~449 (2008), 한국CDE학회