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Global Optimal Locally Weighted Learning-Based Identification Modeling for Azimuth Stern Drive Tug Manoeuvring

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Cognitive Systems and Signal Processing (ICCSIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

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Abstract

This paper presents a black-box modeling approach, locally weighted learning (LWL), for tug handling simulator. Concerned with the problem of parameter drifting and unmodeled dynamics in the conventional mechanism modeling, LWL is proposed for Delta Linda tug to learn the mapping between the input and output directly and employs the empirical regression model instead of the real physical model. Compared with BPNN prediction, the validity and usefulness of the algorithm are illustrated with a 3 degree-of-freedom of Delta Linda tug and LWL is proved to be an accurate manoeuvring modeling tool.

J. Ren—This work was supported by the National 863 project, No. 2015AA016404; National Natural Science Foundation of China, No. 51109020, 51179019, 61374114; Basic Research Project of China Transport Department, No. 2014329225370; The Fundamental Research Program for Key Laboratory of the Education Department of Liaoning Province, No. LZ2015006; The Fundamental Research Funds for the Central Universities, No. 3132016310, 3132016311, 3132016313.

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Author information

Authors and Affiliations

  1. Navigation College, Dalian Maritime University, Dalian, China

    Weiwei Bai, Junsheng Ren, Tieshan Li & C. L. Philip Chen

  2. Second Clinical College, Dalian Medical University, Dalian, China

    Chuan Che

  3. Faculty of Science and Technology, Computer and Information Science, University of Macau, Macau, China

    C. L. Philip Chen

Authors
  1. Weiwei Bai
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  2. Junsheng Ren
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  3. Chuan Che
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  4. Tieshan Li
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  5. C. L. Philip Chen
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Corresponding author

Correspondence to Junsheng Ren .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Dewen Hu

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Bai, W., Ren, J., Che, C., Li, T., Chen, C.L.P. (2017). Global Optimal Locally Weighted Learning-Based Identification Modeling for Azimuth Stern Drive Tug Manoeuvring. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_56

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

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