Radial Basis Function Neural Network and Prediction of Exchange Rate

  • Ming Fang
  • Chiu-Lan ChangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)


This study aims to predict the exchange rat based on radial basis function neural network (RBFNN). Concerning the selected sample point, it only responds to the inputs of neighboring samples and hence has better approximation performance and overall optimization than other forwarding networks, in addition to being simple in structure and fast in training speed. RBFNN in empirical risk minimization methods has been studied, their adaptability to exchange rate prediction has been explored, and these methods have been verified by exchange rate data. The contributions are proposing an exchange rate prediction algorithm based on RBFNN and more accurately predicting short-term and long-term exchange rate variation trends.


Radial basis function Neural network Prediction 



This research was supported by the Education Department of Fujian Province, China (Grant number: JZ160491); Social Science Fund of Fujian Province, China (Grant number: FJ2018B075); Fuzhou University of International Studies and Trade (Grant number: 2018KYTD-14).


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Fuzhou University of International Studies and TradeChangle, Fuzhou CityChina

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