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Proceedings Paper

Decompositional hierarchical self-organizing networks applied to time series forecasting
Author(s): Thomas E. Sandidge; Cihan H. Dagli
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Paper Abstract

The Decompositional Hierarchical Self-Organizing Network (DHSON) is derived from earlier research which developed the Hierarchical Self-Organizing Network. DHSON decomposes input vectors and creates a separate multi-layer 1D self-organized mapping for each component. This approach eliminates the scaling problems typical of Kohonen-like architectures. The end objective is for DHSON to prepare input data for presentation to recurrent networks developed through evolutionary strategies by reducing dimensionality, deriving an effective data encoding for parallel processing, and/or reducing complexity within a data set.

Paper Details

Date Published: 4 April 1997
PDF: 8 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271534
Show Author Affiliations
Thomas E. Sandidge, Univ. of Missouri/Rolla (United States)
Cihan H. Dagli, Univ. of Missouri/Rolla (United States)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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