Network-centric decision architecture for financial or 1/f data models

Holger M. Jaenisch; James W. Handley; Stoney Massey; Carl T. Case; Claude G. Songy

doi:10.1117/12.451005

6 December 2002 Network-centric decision architecture for financial or 1/f data models

Holger M. Jaenisch, James W. Handley, Stoney Massey, Carl T. Case, Claude G. Songy

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.451005
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

Citation Download Citation

Holger M. Jaenisch, James W. Handley, Stoney Massey, Carl T. Case, and Claude G. Songy "Network-centric decision architecture for financial or 1/f data models", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.451005

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