Método de segmentações geométricas sucessivas para treinamento de redes neurais artificiais

Abstract

This work presents a technique for Artificial Neural Network (ANN) training, able to get the network parameters from the available data for training, without establishing the network architecture a priori, called Successive Geometric Segmentation Method (SGSM). The SGSM groups the data of each class into hyperboxes (HB) aligned in accordance with the largest axis of its points distribution. If the HB are linearly separable, a separating hyperplane may be identified resulting a neuron. If it is not, a segmentation technique is applied to divide the data into smaller classes for new HB. For each subdivision new neurons are added to the network. The tests show a rapid method with high success rate.