A State Space Search Algorithm and its Application to Learn the Short-Term Foreign Exchange Rates
Document Type
Article
Publication Date
2008
Publication Title
Applied Mathematical Sciences
Abstract
We propose the use of a state space search algorithm of the discretetime recurrent neural network to learn the short-term foreign exchange rates. By searching in the neighborhood of the target trajectory in the state space, the algorithm performs nonlinear optimization learning process to provide the best feasible solution for the nonlinear least square problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. The stability properties of the algorithm are also discussed. The empirical results show that our method is simple and effectively in learning the short-term foreign exchange rates and is applicable to other applications.
Repository Citation
Leong Kwan Li & Sally S. L. Shao. (2008). A State Space Search Algorithm and its Application to Learn the Short-Term Foreign Exchange Rates. Applied Mathematical Sciences, 2(35), 1705-1728.
Original Citation
Leong Kwan Li & Sally S. L. Shao. (2008). A State Space Search Algorithm and its Application to Learn the Short-Term Foreign Exchange Rates. Applied Mathematical Sciences, 2(35), 1705-1728.
Volume
2
Issue
35