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A prediction method for deck-motion based on online least square support vector machine and genetic algorithm

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Abstract

The prediction accuracy and prediction time are key elements that determine the landing safety of the carrier-based aircraft when ship motions in six-degree freedom are difficult to restrain. The classical prediction methods, such as auto-regressive and moving average model (ARMA) and radical basis function neural network (RBF-NN), always suffer from the drawbacks of short prediction time and low accuracy caused by the nonlinearity and randomness of deck-motion. Aiming to lengthen prediction time and improve prediction accuracy, an online prediction method based on Least Square Support Vector Machine (LSSVM) is proposed with the comprehensive consideration of the characters of deck-motion and inertial navigation system (INS)—an instrument to measure motions including deck-motion. For the sequentiality and timeliness of deck-motion, the proposed online LSSVM prediction method is divided into two stages—information accumulation stage and information window stage. To acquire optimal parameters for LSSVM—the number of samples, the length of sample, the parameter for kernel function and the penalty factor, genetic algorithm (GA) is adopted; in addition, the fitness function for GA is designed according to the periodicity of deck-motion. The prediction tests are conducted with data from deck-motion models and sea trail, respectively, and the results indicate that the proposed method can provide a preferable result compared with those methods based on ARMA, RBF-NN and Particle Swarm Optimization and Kernel Extreme Learning Machine (PSO-KELM) when the data mapping relations are nonlinear and changeable.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation (61273056), the Six talent peaks project of Jiangsu Province (2016-HYGC-001), the Qingdao National Laboratory for Marine Science and Technology (Grant No. QNLM2016ORP0406) and the Taishan Scholar Project Funding (Grant No. TSPD20161007).

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Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People’s Republic of China

    Xixiang Liu, Qiming Wang, Rong Huang, Songbing Wang & Xianjun Liu

  2. Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, 210096, People’s Republic of China

    Xixiang Liu, Qiming Wang, Rong Huang, Songbing Wang & Xianjun Liu

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  1. Xixiang Liu
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  2. Qiming Wang
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  3. Rong Huang
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  4. Songbing Wang
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  5. Xianjun Liu
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Correspondence to Xixiang Liu.

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Liu, X., Wang, Q., Huang, R. et al. A prediction method for deck-motion based on online least square support vector machine and genetic algorithm. J Mar Sci Technol 24, 382–397 (2019). https://doi.org/10.1007/s00773-018-0557-z

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  • DOI: https://doi.org/10.1007/s00773-018-0557-z

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