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RBF Model Based on the Improved KELE Algorithm

Received: 4 May 2017     Published: 4 May 2017
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Abstract

Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously.

Published in Science Journal of Business and Management (Volume 5, Issue 3)
DOI 10.11648/j.sjbm.20170503.12
Page(s) 101-104
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Locally Liner Embedding, Kernel Entropy Component Analysis, Kernel Entropy Liner Embedding

References
[1] A. Babu and S. Reddy, "Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron," Journal of Stock & Forex Trading, vol. 2015, 2015.
[2] J. Luo and L. Chen, "Realised Volatility Forecasts for Stock Index Futures Using the HAR Models with Bayesian Approaches," China Accounting and Finance Review, vol. 18, pp. 1-29, 2016.
[3] R. Hafezi, J. Shahrabi, and E. Hadavandi, "A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price," Applied Soft Computing, vol. 29, pp. 196-210, 2015.
[4] W. Zhang, C. Li, Y. Ye, W. Li, and E. W. Ngai, "Dynamic business network analysis for correlated stock price movement prediction," IEEE Intelligent Systems, vol. 30, pp. 26-33, 2015.
[5] Ca'Zorzi M, Kocięcki A, Rubaszek M. Bayesian forecasting of real exchange rates with a Dornbusch prior [J]. Economic Modelling, 2015, 46: 53-60.
[6] Li Q. How to forecast exchange rate, an unanswered puzzle [J]. 2010.
[7] A. Babu and S. Reddy, "Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron," Journal of Stock & Forex Trading, vol. 2015, 2015.
[8] R. Jenssen, "Kernel entropy component analysis," IEEE transactions on pattern analysis and machine intelligence, vol. 32, pp. 847-860, 2010.
[9] B. Shekar, M. S. Kumari, L. M. Mestetskiy, and N. F. Dyshkant, "Face recognition using kernel entropy component analysis," Neurocomputing, vol. 74, pp. 1053-1057, 2011.
[10] Y. Huang and G. Kou, "A kernel entropy manifold learning approach for financial data analysis," Decision Support Systems, vol. 64, pp. 31-42, 2014.
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  • APA Style

    Chen Xiu-rong, Tian Yi-xiang. (2017). RBF Model Based on the Improved KELE Algorithm. Science Journal of Business and Management, 5(3), 101-104. https://doi.org/10.11648/j.sjbm.20170503.12

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    ACS Style

    Chen Xiu-rong; Tian Yi-xiang. RBF Model Based on the Improved KELE Algorithm. Sci. J. Bus. Manag. 2017, 5(3), 101-104. doi: 10.11648/j.sjbm.20170503.12

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    AMA Style

    Chen Xiu-rong, Tian Yi-xiang. RBF Model Based on the Improved KELE Algorithm. Sci J Bus Manag. 2017;5(3):101-104. doi: 10.11648/j.sjbm.20170503.12

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  • @article{10.11648/j.sjbm.20170503.12,
      author = {Chen Xiu-rong and Tian Yi-xiang},
      title = {RBF Model Based on the Improved KELE Algorithm},
      journal = {Science Journal of Business and Management},
      volume = {5},
      number = {3},
      pages = {101-104},
      doi = {10.11648/j.sjbm.20170503.12},
      url = {https://doi.org/10.11648/j.sjbm.20170503.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20170503.12},
      abstract = {Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - RBF Model Based on the Improved KELE Algorithm
    AU  - Chen Xiu-rong
    AU  - Tian Yi-xiang
    Y1  - 2017/05/04
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sjbm.20170503.12
    DO  - 10.11648/j.sjbm.20170503.12
    T2  - Science Journal of Business and Management
    JF  - Science Journal of Business and Management
    JO  - Science Journal of Business and Management
    SP  - 101
    EP  - 104
    PB  - Science Publishing Group
    SN  - 2331-0634
    UR  - https://doi.org/10.11648/j.sjbm.20170503.12
    AB  - Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China

  • School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China

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