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Ship Power Load Prediction Based on RST and RBF Neural Networks

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

Rough Set Theory (RST) is a powerful mathematics tool, which can deal with fuzzy and uncertain knowledge, and radial basis function (RBF) neural network has the ability to approach any nonlinear function precisely. According to the non-linear relation characteristics of ship power load, a short-term load prediction method based on RST and RBF neural network is presented in this paper. Using RST on the advantage of data analysis, the important input nodes can be selected, followed by a second stage selecting the important centers and leaning the weights of hidden nodes. The experimental results proved that this method could achieve greater predictive accuracy and generalization ability.

This work was supported by National Natural Science Foundation of China (60074004) and Science Foundation of Shanghai Education (03IK09, 04IK02).

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Author information

Authors and Affiliations

  1. Department of Electrical and Automation, Shanghai Maritime University, Shanghai, 200135, China

    Jianmei Xiao, Tengfei Zhang & Xihuai Wang

Authors
  1. Jianmei Xiao
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  2. Tengfei Zhang
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  3. Xihuai Wang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Xiao, J., Zhang, T., Wang, X. (2005). Ship Power Load Prediction Based on RST and RBF Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_103

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  • DOI: https://doi.org/10.1007/11427469_103

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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