Decompositional Hierarchical Self-Organizing Networks Applied to Time Series Forecasting
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.
Recommended Citation
T. E. Sandidge and C. H. Dagli, "Decompositional Hierarchical Self-Organizing Networks Applied to Time Series Forecasting," Proceedings of SPIE - The International Society for Optical Engineering, vol. 3077, pp. 27 - 34, Society of Photo-optical Instrumentation Engineers, Dec 1997.
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
01 Dec 1997
