Documents disponibles écrits par cet auteur

Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training / Moreno, Félix in IEEE transactions on industrial electronics, Vol. 56 N° 8 (Août 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N° 8 (Août 2009) . - pp. 3253 - 3263 Titre : Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training Type de document : texte imprimé Auteurs : Moreno, Félix, Auteur ; Alarcon, Jaime, Auteur ; Salvador, Rubén, Auteur Article en page(s) : pp. 3253 - 3263 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Neural  network  hardware  implementation  Run-time  learning  recognition Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4926188 [article] Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training [texte imprimé] / Moreno, Félix, Auteur ; Alarcon, Jaime, Auteur ; Salvador, Rubén, Auteur . - pp. 3253 - 3263. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N° 8 (Août 2009) . - pp. 3253 - 3263 Mots-clés : Neural  network  hardware  implementation  Run-time  learning  recognition Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4926188