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Financial simulation system using a higher order trigonometric polynomial neural network group model

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3 Author(s)
Jing Chun Zhang ; Dept. of Comput. & Inf. Syst., Univ. of Western Sydney, Campbelltown, NSW, Australia ; Ming Zhang ; Fulcher, J.

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

Published in:

Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997

Date of Conference:

23-25 Mar 1997