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MISNC 2014: Multidisciplinary Social Networks Research pp 404–416Cite as

A Hybrid Algorithm by Combining Swarm Intelligence Methods and Neural Network for Gold Price Prediction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 473))

Abstract

This paper attempts to enhance the learning performance of radial basis function neural network (RBFN) through swarm intelligence methods and self-organizing map (SOM) neural network (SOMnet). Further, the particle swarm optimization (PSO) and genetic algorithm (GA)-based method (i.e., PG approach) is employed to train RBFN. The proposed SOMnet + PG approach (called: SPG) algorithm combines the automatically clustering ability of SOMnet with PG approach. The simulation results revealed that SOMnet, PSO, and GA methods can be integrated ingeniously and redeveloped into a hybrid algorithm which aims for obtaining the best accurate learning performance among other algorithms in this study. On the other hand, method evaluation results for two benchmark problems and a gold price prediction case showed that the proposed SPG algorithm outperforms other algorithms and the auto-regressive integrated moving average (ARIMA) models in accuracy.

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Author information

Authors and Affiliations

  1. Department of Business Administration, DE LIN Institute of Technology, No. 1, Ln 380, Qingyun Rd., New Taipei City, 23654, Taiwan

    Zhen-Yao Chen

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  1. Zhen-Yao Chen
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Editor information

Editors and Affiliations

  1. National University of Kaohsiung, Taiwan

    Leon Shyue-Liang Wang

  2. Chung-Ang University, Seoul, Korea

    Jason J. June

  3. Dept of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

    Chung-Hong Lee

  4. Osaka University, Japan

    Koji Okuhara

  5. National University of Kaohsiung, Dept of Information Management, Kaohsiung, Taiwan

    Hsin-Chang Yang

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Chen, ZY. (2014). A Hybrid Algorithm by Combining Swarm Intelligence Methods and Neural Network for Gold Price Prediction. In: Wang, L.SL., June, J.J., Lee, CH., Okuhara, K., Yang, HC. (eds) Multidisciplinary Social Networks Research. MISNC 2014. Communications in Computer and Information Science, vol 473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45071-0_33

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  • DOI: https://doi.org/10.1007/978-3-662-45071-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45070-3

  • Online ISBN: 978-3-662-45071-0

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