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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

3PGCIC 2017: Advances on P2P, Parallel, Grid, Cloud and Internet Computing pp 199–205Cite as

RBF Model Based on the KECDBN

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 13))

Abstract

For the dilemma in determining the structure and shortage of supervision in learning process of the deep learning feature extraction algorithm CDBN, this paper proposes a method to improve this situation by adopting KECA to extract features more deeply and effectively and then we take the extracted features as the input of RBF. We use foreign exchange series to conduct the em-pirical study which shows that the accuracy of the KECDBN-RBF model is obviously improved compared to single RBF.

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Acknowledgement

This work is supported by Guangxi Key Laboratory of Cryptography and Information Security, Grant/Award Number: GCIS201610.

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Authors and Affiliations

  1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiurong Chen & Yixiang Tian

  2. Engineering University of CAPF, Xi’an, China

    Xuan Wang & Xuguang Wu

Authors
  1. Xiurong Chen
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  2. Yixiang Tian
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  3. Xuan Wang
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  4. Xuguang Wu
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Corresponding author

Correspondence to Yixiang Tian .

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Editors and Affiliations

  1. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  2. Open University of Catalonia, Barcelona, Spain

    Santi Caballé

  3. Fukuoka Institute of Technology (FIT), Fukuoka, Japan

    Leonard Barolli

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Chen, X., Tian, Y., Wang, X., Wu, X. (2018). RBF Model Based on the KECDBN. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-69835-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69834-2

  • Online ISBN: 978-3-319-69835-9

  • eBook Packages: EngineeringEngineering (R0)

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