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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11485

Title: A bilayer feed-forward artificial neural network for exchange rate prediction
Authors: Ackora-Prah, Joseph
Sakyi, Adu
Ayekple, Yao Elikem
Keywords: Exchange Rates
Levenberg –Marquardt
Neural Networks
Issue Date: Aug-2014
Publisher: Australian Journal of Applied Mathematics
Citation: Australian Journal of Applied Mathematics, August - 2014
Abstract: A feed-forward Neural Network is an interconnection of perceptrons in which data and computations flow in a single direction from the input data to the outputs. We used a two layer feed-forward network using Levenberg –Marquardt Back propagation Neural Network (LMBNN) to forecast the Ghanaian Cedi –US Dollar rate with Treasury bill rates, money supply, consumer price index and inflation. The results were measured with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the Weighted Absolute Percentage Error (WAPE). After careful and extensive training, validation and testing, the Artificial Neural Network (ANN) produced MSE, RMSE, WAPE and an R-value of 0.0010, 0.0324, 2.30%, 0.99634 respectively with a prediction accuracy of 97.70%.
Description: An article published in Australian Journal of Applied Mathematics, August - 2014
URI: http://hdl.handle.net/123456789/11485
Appears in Collections:College of Science

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