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Traffic Data Prediction Based on Complex-Valued S-System Model

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

To predict traffic data accurately could make an important role in network management. In order to improve forecasting accuracy, this paper proposes complex-valued S-system model (CVSS) forecast small-scale traffic data. According to the form of CVSS model, complex-valued restricted gene expression programming (CVRGEP) is utilized to search the optimal the representation of CVSS. Complex-valued differential evolution (CVDE) is proposed to evolve the parameters of model. The small-scale traffic data is utilized to test our method. Our method has better prediction performances than neural network (NN), radial basis function neural network (RBF), flexible neural tree (FNT), ordinary differential equation (ODE) and S-system.

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Acknowledgment

This work was supported by the talent project of “Qingtan scholar” of Zaozhuang University, the PhD research startup foundation of Zaozhuang University (No. 2014BS13), and foundation of Zaozhuang University (No. 2015YY02).

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Authors and Affiliations

  1. School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China

    Bin Yang & Wei Zhang

Authors
  1. Bin Yang
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  2. Wei Zhang
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Corresponding author

Correspondence to Wei Zhang .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Yang, B., Zhang, W. (2020). Traffic Data Prediction Based on Complex-Valued S-System Model. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_37

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60801-9

  • Online ISBN: 978-3-030-60802-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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