The Application Of Genetic Algorithms And Neural Networks In The Exchange Rate Forecast

Economic system is a complicated non-linear system. The nonlinear interaction in economic system forms the irregular rise and fall of Macroeconomic economic variables. The mechanism of exchange rate formation is also a non-linear system, meanwhile has a high degree of complexity. So it's difficult to resolve by accurate mathematical models. Then, we hope that a non-linear model can be modeled, parameters of which changes normally vary with changes of forecasting environment. Artificial neural net affords a good possibility to solve this problem. Its black-box strategy loses sight of the determinant of non-linear equation, and directly uses the input / output variables of network to train and adjust the network. This method can determine interaction and influence in economic variables for forecast purposes.Moreover, in finance system, the training signal is fuzzy, and the data is noisy, so it's very difficult to give a quantitative evaluation to every execution. For bridging over the difficulties and improving system performance, we can use genetic algorithm to optimize artificial neural net.Aiming at non-linear character of exchange rate variety and the limitation of classical BP artificial neural net on forecast application, this paper makes improvement on BP net model, and uses genetic algorithm to optimize BP artificial neural net to get a coupling model of real coded accelerating genetic algorithm and BP net, then, makes a special study. Our results show that this coupling model gets a good improvement in calculation accuracy and computing speed. Further more, applying to long-term exchange rate forecast, this model has better adaptability and accuracy. In the end, we learn that RAGA-BP model has good value on forecasting of complicated and non-linear exchange rate system.