Forecasting the RMB Exchange Regime

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{XiaobingFeng:2011:ICFCSE,
  author =       "Xiaobing Feng",
  title =        "Forecasting the RMB Exchange Regime",
  booktitle =    "International Conference on Future Computer Science
                 and Education (ICFCSE 2011)",
  year =         "2011",
  month =        aug,
  pages =        "633--636",
  size =         "4 pages",
  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 normalised,
                 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.",
  keywords =     "genetic algorithms, genetic programming, BP network,
                 RMB exchange regime forecasting, back-propagation
                 network, exchange rate forecast method, genetic
                 programming approach, radial basis function neural
                 network, backpropagation, exchange rates, radial basis
                 function networks",
  DOI =          "doi:10.1109/ICFCSE.2011.158",
  notes =        "page635 'Thus GP outperforms ANN.'

                 Also known as \cite{6041775}",
}

Genetic Programming entries for Xiaobing Feng

Citations