RBF Model Based on the Improved KELE Algorithm

Chen Xiu-rong; Tian Yi-xiang

doi:doi:10.11648/j.sjbm.20170503.12

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RBF Model Based on the Improved KELE Algorithm

Chen Xiu-rong, Tian Yi-xiang

Published in Science Journal of Business and Management (Volume 5, Issue 3)

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

Chen Xiu-rong

School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Tian Yi-xiang

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

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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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download