A Neural Architecture for the Identification of Number Sequences

Juan MorenoGabriel SebastiánMiguel Angel FernándezAntonio Fernández-Caballero

This paper describes an architecture based on spatio-temporal networks that identifies sequences of numbers. This architecture incorporates an input layer that transforms (by means of a mathematical function) the system's input into a normalized vector that will be applied in a second step to a spatio-temporal network. Finally, the architecture is completed by an output layer using Grossberg's outstar units [1]. We have appreciated our system's complexity to be lower than any other existent method developed to solve problems of this type. By means of this architecture we have implemented a system that reminds a user of the telephone number of a given list, even if the user only remembers part of it, or if the given number contains a series of exchanged digits. The system processes the input and returns the selected telephone number among all the learned ones.

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