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DANS CETTE THESE NOUS NOUS SOMMES INTERESSES A LA CONCEPTION, A L'ANALYSE ET A L'IMPLANTATION D'ALGORITHMES PARALLELES, POUR L'APPRENTISSAGE DE RESEAUX DE NEURONES ARTIFICIELS, SUR DES MACHINES MULTIPROCESSEURS DE TYPE MIMD. PLUS PRECISEMENT, NOUS AVONS ETUDIE LES DEUX MODELES CONNEXIONNISTES SUIVANTS: RESEAUX DE KOHONEN ET RESEAUX DE FONCTIONS A BASE RADIALE, PLUS CONNUS SOUS LEUR NOM ANGLAIS DE RESEAUX RBF (RADIAL BASIS FUNCTION). LA PREMIERE PARTIE DE CE MANUSCRIT CONTIENT UNE BREVE DESCRIPTION DES OUTILS DE PROGRAMMATION PARALLELE LES PLUS RECENTS. LES DEUX PARTIES SUIVANTS CONSTITUE CHACUNE DE TROIS CHAPITRES, PRESENTENT NOS TRAVAUX SUR LA PARALLELISATION, D'UNE PART DE L'ALGORITHME DE KOHONEN, ET D'AUTRE PART DE L'ALGORITHME OLS (ORTHOGONAL LEAST SQUARES) UTILISE POUR L'APPRENTISSAGE DES RESEAUX RBF. UNE QUATRIEME PARTIE PRESENTE UNE APPLICATION DE CES RESEAUX DE NEURONES A LA PREDICTION DE POLLUTION EN MILIEU INDUSTRIEL
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