Time series: theory and methods
Time series: theory and methods
Connectionist projection pursuit regression
Computational Economics
A neural network approach to long-run exchange rate prediction
Computational Economics - Special issue on computational finance: papers from the IFAC workshop on computing in economics and finance, held at the Univ. of Amsterdam, June 1994
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Remote timing attacks are practical
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
GA optimization of Petri net-modeled concurrent service systems
Applied Soft Computing
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The use of neural networks trained by a new hybrid algorithm is employed on forecasting the Greek Foreign Exchange-Rate Market. Four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DEM), the French Franc (FF) and the British Pound (GBP), versus the Greek Drachma, were used as experimental data. The proposed algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptron (MLP) neural networks. The goal of this effort is to predict, as accurately as possible, exchange-rates future behavior. Simulation results show that the method gives highly successful results, while the diversification of the structure between the four currencies has no effect on the performance.