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Using Additive Expression Programming for System Identification

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Intelligent Computing Theories and Methodologies (ICIC 2015)

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Abstract

The system identification is crucially important process, which could develop the mathematical representation of physical system from observed data. In this paper, a new model, called additive expression tree (AET) model is proposed to encode the linear and nonlinear systems. A new structure-based evolutionary algorithm and artificial bee colony (ABC) are used to optimize the architecture and parameters of additive expression tree model, respectively. Experimental results demonstrate that our proposed model and hybrid approach could identify the linear/nonlinear systems effectively.

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Acknowledgment

This work was supported the PhD research startup foundation of Zaozhuang University (No.1020702).

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Authors and Affiliations

  1. School of Information Science and Engineering, Zaozhuang University, Zaozhuang, People’s Republic of China

    Bin Yang

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  1. Bin Yang
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Correspondence to Bin Yang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Yang, B. (2015). Using Additive Expression Programming for System Identification. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_67

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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