A neural architecture for the identification of number sequences

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 (1982). 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 a telephone number, out of a given list, if the user enter the number wrongly, i.e. partially or with a series of exchanged digits