An architecture of self-organizing map for temporal signal processing and its application to a Braille recognition task

A self-organizing map (SOM) performs a mapping of an object preserving its topological relations between input and output spaces, and also can be seen as a coordinate transformer that preserves adjacency relations. Since the standard SOM cannot deal with temporal data intrinsically, in this paper we provide new feedback pathways around the competitive layer to refer to context information of the past history. An extra output layer is added next to the competitive layer to represent secondary candidates and a quantitative measure of confidence. From the viewpoint of its structural similarities, we refer to this architecture as an Elman-type feedback SOM. In order to clarify the effectiveness of the proposed model, we then adopt a temporal signal processing task of Braille recognition. Braille is a character set for visually impaired people made up of 6 dots on a 3 by 2 grid. When Braille is read by running the fingertip over the characters, the vertical axis can be seen as providing spatial information and the horizontal axis as providing temporal information. As a result of computer simulations with partially common four city names, we confirm that the proposed model can recognize them appropriately. In addition, we find that this approach is robust to both the temporal elasticity and spatial displacement. Moreover, when we conduct an analysis with the neuro-bar model, we find that the Braille recognition task is executed based on a series of state transition along a type of pathway that has been developed during training. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(3): 62– 71, 2007; Published online in Wiley InterScience (). DOI 10.1002sscj.20260