Title :
Financial data simulation using A-PHONN model
Author_Institution :
Christopher Newport Univ., Newport News, VA, USA
Abstract :
A new model, called Adaptive Multi-Polynomial Higher Order Neural Network (A-PHONN), has been developed. Using Sun workstation, C++, and Motif, an A-PHONN simulator has been built as well. Real world data always can not be simply simulated very well by single polynomial function. So the ordinary higher order neural networks could fail to simulate such complicated real world data. But A-PHONN model can simulate multipolynomial functions with coefficient adaptively adjustable, it makes A-PHONN model can achieve more accuracy for real world data simulation. The comparison experiments between A-PHONN and ordinary higher order neural network also shows that A-PHONN always can have 2-50% more accuracy than ordinary higher order neural networks
Keywords :
digital simulation; financial data processing; neural nets; polynomials; A-PHONN model; A-PHONN simulator; Adaptive Multi-Polynomial Higher Order Neural Network; C++; Motif; Sun workstation; financial data simulation; high-order neural networks; multipolynomial functions; Atmospheric modeling; Computational modeling; Economic forecasting; Neural networks; Neurons; Polynomials; Predictive models; Sun; USA Councils; Workstations;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938439