Abstract
To get the best course and speed navigating in stormy waves, establish the sea-keeping assessment model based on Support Vector Machine method, verify the accuracy of the model with sea-keeping estimation equation, and finally apply it in decision making of maneuvering. It turns out that the assessment model works well. The conclusions provide references for maneuvering in stormy waves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Li, S.Z., Wang, F.W., Liu, Q., Qi, Z.: SeakeepingEvaluation Based on BP Neural Network. Journal of Dalian Maritime University 38(1), 15–17 (2012) (in Chinese)
Wu, Z.Q.: TheResearch on the Evaluation for Investment Risk of Freeway Project Based on Support Vector Machine. Changsha Technical University (2009) (in Chinese)
Ai, N., Wu, Z.W., Ren, J.H.: Support Vector Machine and Artificial Neural Network. Journal of Shandong University of Technology 19(5), 45–49 (2005) (in Chinese)
Pan, X., Yang, R.Y.: The Research on Neural Networks with Enhanced Generalization and Support Vector Machine. Journal of Anqing Teachers College 13(1), 32–36 (2007) (in Chinese)
Bai, P., Zhang, X.B.: Theory of Support Vector Mechanism and the Examples of Engineering Application. Xi’an Electronic and Technology University Press (2008) (in Chinese)
Chang, Z., Lu, J.: Application of Support Vector Machine in the Evaluation of Dry Ports Investment Risk. Journal of Dalian Maritime University 38(2), 48–51 (2012) (in Chinese)
Xiong, W.H., Mao, X.F., Li, Y.J.: Review on Evaluation Methods and Criteria for Sea-keeping of Ships. Ship & Ocean Engineering 36(4), 43–44 (2007) (in Chinese)
Vapnik, V.: An Overview of Statistical Learning Theory. IEEE Transaction on Neural Networks 10(5), 988–999 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Qi, Z., Chang, Z., Song, H., Zhang, X. (2014). Application of Support Vector Machine in the Decision-Making of Maneuvering. In: Sun, Xh., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2014. Lecture Notes in Computer Science, vol 8631. Springer, Cham. https://doi.org/10.1007/978-3-319-11194-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-11194-0_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11193-3
Online ISBN: 978-3-319-11194-0
eBook Packages: Computer ScienceComputer Science (R0)