SciELO - Scientific Electronic Library Online

vol.18 issue2Modelo Neuro-Fuzzy Hierárquico Politree com aprendizado por reforço para agentes inteligentes author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Sba: Controle & Automação Sociedade Brasileira de Automatica

Print version ISSN 0103-1759


SIMOES, Alexandre da Silva  and  COSTA, Anna Helena Reali. Aprendizado não-supervisionado em redes neurais pulsadas de base radial: um estudo da capacidade de agrupamento para a classificação de pixels. Sba Controle & Automação [online]. 2007, vol.18, n.2, pp. 251-264. ISSN 0103-1759.

Pulsed neural networks - networks that encode information in the timing of spikes - has been studied as a new and promising approach in the artificial neural networks paradigm, emergent from the cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. A learning algorithm was successfully applied to this pulsed network, which was able to map a sequence of input pulses into a sequence of output pulses. More recently, a method based on the use of Gaussian receptive fields was proposed to encode constant data into a temporal sequence of spikes. This method allowed this network to deal with computational data. The learning process of this new network is not completely understood and deeper investigations are necessary in order to situate this model in the machine learning context and also to establish the network abilities and limitations. This work investigates this new classifier and presents a study of the network capability in solving the three-dimensional clustering task, particularly looking for establishing its application domains and horizons in the computer vision field.

Keywords : Pulsed neural networks; unsupervised learning; clustering; computer vision.

        · abstract in Portuguese     · text in Portuguese     · Portuguese ( pdf epdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License