影响因子：7.8
DOI码：10.1016/j.asoc.2023.110487
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Applied Soft Computing
刊物所在地：荷兰
项目来源：The work is supported by the following project grants, National Key Research and Development Program
关键字：Offshore platform motion; Deep learning model; Modal decomposition; Ant lion algorithm; Chaotic mapping; Quantum computing
摘要：Accurate prediction of floating offshore platform motion (FOPM) is very important to control the movement of the platform and the normal operation of the equipment on the platform. However, the accurate prediction of FOPM is very difficult, due to the coupling effect of the mooring system, operation system, wind, wave, and current. Therefore, in order to obtain more accurate prediction results, firstly, the Convolutional LSTM (ConvLSTM) network is introduced to simulate FOPM nonlinear dynamical system, design input vector coding rules, considering the characteristics of complex time-varying nonlinear and space non-stationary of FOPM. The EEMD is applied for modal decomposition for time series of FOPM to reduce the nonlinearity of time series. Then the FOPM-EEMD-ConvLSTM forecasting model is proposed by designing the input sequence matrix and prediction architecture. Considering the defects of ALO algorithm, based on quantum computing and chaotic mapping, the quantum global search algorithm (QRS) and ant lion trap Chaos reconstruction mechanism (ALTCR)are designed, and chaotic quantum ant lion optimization algorithm (CQALO) is proposed and use to optimate the hyperparametric of FOPM-EEMD- ConvLSTM. Consequently, a hybrid forecasting approach of FOPM integrating FOPM-EEMD- ConvLSTM and CQALO was established, namely FOPM-EEMD-ConvLSTM-CQALO. Finally, the sway and heave data of a floating offshore platform serving in the ocean is used to carry out prediction experiments to test the performance of the proposed new prediction approach. The test results indicate that the prediction model established in this paper has higher prediction accuracy and stronger robustness than the comparison model selected in this paper and the CQALO obtains more appropriate hyperparameters than the comparison algorithm applied in selecting the hyperparameters of FOPM-EEMD- ConvLSTM.
备注：The work is supported by the following project grants, National Key Research and Development Program of China (2019YFB1504403); the Scientific and Technical Program of China National Offshore Oil Corporation under contract No. LSZX-2020-HN-05-04; High-tech Ship Technology Project, China (MC-202030-H04); Heilongjiang Excellent Youth Fund Project, China (YQ2021E015); National Natural Science Foundation of China (No. 51509056); and Ministry of Science and Technology, Taiwan (MOST 111-2410-H-161-001
合写作者：Mei-Rong Jiang,李明伟,徐瑞喆
第一作者：李达
论文类型：期刊论文
通讯作者：洪维强
论文编号：110487
学科门类：工学
文献类型：J
卷号：144
ISSN号：1568-4946
是否译文：否
发表时间：2023-01-01
收录刊物：SCI