Analysis Of Exchange Rate Forecast Model of US Dollar Against RMB Based on BP Neural Network
DOI:
https://doi.org/10.54097/ek1jn394Keywords:
BP neural network; deep learning; predictionAbstract
Judging from the economic development situation in recent years, the uncertainty of exchange rate fluctuation will be affected by the exchange rate, not only for domestic and foreign trade but also for the cost of enterprises and individuals. Therefore, in view of this crucial problem, in this study, BP(Back Propagation) synthetic neural network is drilled by the opening price of the day in order to predict the closing price in the next day. The method is to divide 5000 groups of data into 4000 training sets and 1000 test sets. Single hidden layer neural network is adopted, and the number of nodes is determined to be 6 and 15 through the experiment of the influence of the quantity of nodes on model performance. The simulation test show that the root mean square errors in the prediction set and the verification set are very small, which are 0.0004 and 0.0006 respectively, so the neural network has good accuracy in forecasting the closing price of the next day, and some opinions on this model are also given.
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