The Use of a Hybrid Neural System for the Classification of Stars

Abstract

A hybrid neural approach for a fully automated spectral and luminosity classification of stars is presented. The hybrid neural system (HNS) integrates a neural classifier and a semantic network used for similarity based reasoning and conceptual knowledge representation, respectively. The semantic net is designed in accordance to the two-dimensional spectral and luminosity MK-classification of stars. In this paper, the architecture, functionality and application of the HNS in astronomy are presented. The functional capabilities and results of stellar classification of the HNS show significant improvements compared to conventional astronomical techniques. After knowledge aquisition is once completed, the system classifies stellar objects very fast, reliably and without any need for further pre-classification of them. The pure neural classification is completed with information retrieval using the respective attached semantic net. Moreover the HNS is also able to compare classes of stars without forcing the user to give any raw input data and special knowledge about relations between these classes. This function relies on the integration of both networks using spreading-activation. The HNS provides the first application of a hybrid neural approach in the area of astronomical classification of stellar objects. Aspects for further work on the HNS are briefly proposed.