• DocumentCode
    1748807
  • Title

    Financial data simulation using A-PHONN model

  • Author

    Zhang, Ming

  • Author_Institution
    Christopher Newport Univ., Newport News, VA, USA
  • Volume
    3
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1823
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.938439
  • Filename
    938439