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Proceedings Paper

Network-centric decision architecture for financial or 1/f data models
Author(s): Holger M. Jaenisch; James W. Handley; Stoney Massey; Carl T. Case; Claude G. Songy
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Paper Abstract

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

Paper Details

Date Published: 6 December 2002
PDF: 12 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.451005
Show Author Affiliations
Holger M. Jaenisch, SPARTA, Inc. (United States)
James W. Handley, SPARTA, Inc. (United States)
Stoney Massey, Alabama A&M Univ. (United States)
Carl T. Case, SPARTA, Inc. (United States)
Claude G. Songy, SPARTA, Inc. (United States)

Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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