ALGORITMO DE ENTRENAMIENTO DE UNA NEURONA ARTIFICIAL PERCEPTRON PARA RECONOCIMIENTO DE CARACTERES, APLICANDO PRINCIPIOS HEURISTICOS

Abstract

In the present work there is approached the topic of recognition of printed characters applying an artificial neuron type perceptron for every letter of the alphabet. Generally, for the solution of problems of recognition of characters it is considered to be a complex problem and very complex structures of neural artificial networks are created by a great quantity of neurons organized in several caps, giving origin to complex algorithms of training that must operate during long periods to find the suitable solution and for other one side also the time of recognition to increase due to the great quantity of calculation that it is necessary to realize to obtain the result. In this work one proposes an algorithm that reduces the time of training and in turn there allows to solve the problem of recognition of a character in an artificial neuron type perceptron applying heuristic principles, which consider own characteristics of the problem of recognition of letters, own characteristics of the letters and others. The obtained results allow us to implement more rapid solutions of recognition of characters due to the great reduction of calculation during the training and also during the functioning, since every alone character needs of an artificial neuron to be recognized. On the other hand this algorithm can be generalized to raise algorithms of training for neural artificial networks multicaps.