- 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}",
