Title :
Financial simulation system using a higher order trigonometric polynomial neural network group model
Author :
Zhang, Jing Chun ; Zhang, Ming ; Fulcher, John
Author_Institution :
Dept. of Comput. & Inf. Syst., Univ. of Western Sydney, Campbelltown, NSW, Australia
Abstract :
A trigonometric polynomial high order neural network financial simulation (THONN) system is presented. The system was written in C, incorporates a user-friendly graphical user interface (GUI), and runs under X-Windows on a Sun workstation. The experimental results show that the THONN group model is able to handle higher frequency, higher order non-linear and discontinuous data. Using the THONN model, the accuracy is about 5%-10% better than conventional trigonometric polynomial neural network models
Keywords :
data handling; feedforward neural nets; financial data processing; graphical user interfaces; human factors; multilayer perceptrons; polynomials; C language; Sun workstation; THONN; X-Windows; discontinuous data; financial simulation system; graphical user interface; higher order nonlinear data; multilayer neural network; trigonometric polynomial high order neural network; user-friendly; Artificial neural networks; Computational modeling; Displays; Economic forecasting; Electronic mail; Graphical user interfaces; Neural networks; Polynomials; Sun; Workstations;
Conference_Titel :
Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-4133-3
DOI :
10.1109/CIFER.1997.618934
