Construction and verification of nonparameterized ship motion model based on deep neural network

• 발행기관: 한국항해항만학회
• 저자명: Wang Zongkai Im Nam-kyun
• 간행물 정보: 『한국항해항만학회 학술대회논문집』2022 추계학술대회논문집, 170~171쪽, 전체 2쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2022.11.10

영문 초록

A ship’s maneuvering motion model is important in a computer simulation, especially under the trend of intelligent navigation. This model is usually constructed by the hydrodynamic parameters of the ship which are generated by the principles of hydrodynamics. Ship’s motion model is a nonlinear function. By using this function, ships’ motion elements can be calculated, then the ship’s trajectory can be predicted. Deeping neural networks can construct any linear or non-linear equation theoretically if there have enough and sufficient training data. This study constructs some kinds of deep Networks and trains this network by real ship motion data, and chooses the best one of the networks, uses real data to train it, then uses it to predict the ship’s trajectory, getting some conclusions and experiences.

키워드

Ship’s maneuvering motion models Deeping neural networks Predicting ship’s trajectory