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Forecasting the RMB Exchange Regime Using Genetic Programming Approach

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Advances in Education and Management (ISAEBD 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 211))

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Abstract

To resolve the slow convergence and local minimum problem of BP network, an exchange rate forecast method based on Radial Basis Function Neural Network (RBFNN) is proposed.Data on economic variables is normalized, and then is put into the RBFNN in training. Corresponding parameters are got and then the exchange rate is predicted. Detailed simulation results and comparisons with Back-Propagation (BP) network show that, the operation speed of the method is faster and the forecast accuracy is higher than the traditional BP neural network can be achieved obviously. We then use genetic programming approach to achieve a better outcome compared with ANN.

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Author information

Authors and Affiliations

  1. Shanghai JianQiao College, Financial Management College, Shanghai Institute of Foreign Trade, Shanghai, China

    Xiaobing Feng

Authors
  1. Xiaobing Feng
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Editor information

Editors and Affiliations

  1. Education Society, FLAT F, 15th Floor, Block 3, The Sherwood No.8 Fuk Hang, Tsuen Road, Tuen Mun, Hong Kong

    Mark Zhou

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Feng, X. (2011). Forecasting the RMB Exchange Regime Using Genetic Programming Approach. In: Zhou, M. (eds) Advances in Education and Management. ISAEBD 2011. Communications in Computer and Information Science, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23062-2_74

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  • DOI: https://doi.org/10.1007/978-3-642-23062-2_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23061-5

  • Online ISBN: 978-3-642-23062-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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