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.
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