{"id":5949,"date":"2023-09-08T16:46:50","date_gmt":"2023-09-08T14:46:50","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5949"},"modified":"2026-03-30T11:24:29","modified_gmt":"2026-03-30T09:24:29","slug":"uniwin-ann","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-ann\/","title":{"rendered":"Uniwin – ANN"},"content":{"rendered":"

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UNIWIN - R\u00e9seaux de Neurones (classement, r\u00e9gression)<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Inspir\u00e9s des neurones du cerveau, les r\u00e9seaux de neurones artificiels n\u2019ont rien de biologique. Ce sont des fonctions math\u00e9matiques \u00e0 plusieurs param\u00e8tres. L\u2019analogie date des premiers automates propos\u00e9s en 1943 par Warren McCulloch et Walter Pitts. Comme dans les neurones du cerveau o\u00f9 des connexions se cr\u00e9ent, disparaissent ou se renforcent en fonction de diff\u00e9rents stimuli et produisent une action, les r\u00e9seaux de neurones artificiels ajustent des param\u00e8tres (appel\u00e9s poids synaptiques en r\u00e9f\u00e9rence au fonctionnement biologique du cerveau) en fonction de donn\u00e9es d\u2019entr\u00e9e afin de fournir la meilleure r\u00e9ponse possible. En apprentissage machine, un neurone fait une combinaison lin\u00e9aire des entr\u00e9es qu\u2019il re\u00e7oit, \u00e0 laquelle il ajoute une valeur appel\u00e9e biais. Une fonction non lin\u00e9aire, dite d\u2019activation (logistique ou tangente hyperbolique) est alors appliqu\u00e9e \u00e0 la valeur de sortie. Cette valeur est ensuite transmise \u00e0 la couche de neurones suivante si le seuil est franchi. Chaque neurone effectue ainsi un calcul tr\u00e8s rudimentaire et c\u2019est la succession des couches de neurones qui permet d\u2019obtenir des r\u00e9seaux complexes. Durant cette phase dite d\u2019apprentissage, le r\u00e9seau va ajuster automatiquement les param\u00e8tres de chaque neurone, c\u2019est-\u00e0-dire les valeurs des poids et du biais afin de minimiser l\u2019erreur moyenne calcul\u00e9e sur l\u2019ensemble des observations entre la sortie attendue et celle observ\u00e9e. L\u2018hypoth\u00e8se est qu\u2019apr\u00e8s cette phase d\u2019apprentissage, le r\u00e9seau sera capable de traiter de mani\u00e8re satisfaisante de nouvelles observations, dont la sortie est inconnue, en fonction de ce qu\u2019il a appris. Dans un r\u00e9seau de neurones \u00e0 deux couches, la premi\u00e8re couche est constitu\u00e9e d\u2019un ensemble de neurones connect\u00e9s en parall\u00e8le et fournissant un ensemble de sorties, elles-m\u00eames combin\u00e9es pour devenir les entr\u00e9es d\u2019un nouvel ensemble de neurones formant une seconde couche.<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R \u2018neuralnet\u2019.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6443″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

Tableaux<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n
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R\u00e9sum\u00e9 de l’analyse<\/p>\n<\/td>\n<\/tr>\n

\n

Importance des variables explicatives<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sultats pour le jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion (classement – apprentissage )<\/p>\n<\/td>\n<\/tr>\n

\n

Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (classement – apprentissage )<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sultats pour les jeux de validation et de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion (classement – validation )<\/p>\n<\/td>\n<\/tr>\n

\n

Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (classement – validation )<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Graphique du r\u00e9seau<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de l’importance des variables explicatives<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points (pr\u00e9vision)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des fronti\u00e8res (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des fronti\u00e8res (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme de la matrice de confusion (classement – apprentissage )<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme de la matrice de confusion (classement – validation )<\/p>\n<\/td>\n<\/tr>\n

\n

Courbe ROC (classement – apprentissage )<\/p>\n<\/td>\n<\/tr>\n

\n

Courbe ROC (classement – validation )<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique valeurs pr\u00e9vues vs valeurs observ\u00e9es (r\u00e9gression – apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique valeurs pr\u00e9vues vs valeurs observ\u00e9es (r\u00e9gression – validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique r\u00e9sidus vs valeurs pr\u00e9vues (r\u00e9gression – apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique r\u00e9sidus vs valeurs pr\u00e9vues (r\u00e9gression – validation)<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:https%3A%2F%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FR%25e9seaux%2520de%2520neurones%2520artificiels.pdf|title:UNIWIN%20-%20ANN\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Inspir\u00e9s des neurones du cerveau, les r\u00e9seaux de neurones artificiels n\u2019ont rien de biologique. Ce sont des fonctions math\u00e9matiques \u00e0 plusieurs param\u00e8tres. L\u2019analogie date des premiers automates propos\u00e9s en 1943 par Warren McCulloch et Walter Pitts. Comme dans les neurones du cerveau o\u00f9 des connexions se cr\u00e9ent, disparaissent ou se renforcent en fonction de diff\u00e9rents…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5949","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - ANN - FRANCESTAT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/francestat.com\/index.php\/uniwin-ann\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - ANN - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Inspir\u00e9s des neurones du cerveau, les r\u00e9seaux de neurones artificiels n\u2019ont rien de biologique. Ce sont des fonctions math\u00e9matiques \u00e0 plusieurs param\u00e8tres. L\u2019analogie date des premiers automates propos\u00e9s en 1943 par Warren McCulloch et Walter Pitts. 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