{"id":7157,"date":"2019-09-26T16:29:53","date_gmt":"2019-09-26T15:29:53","guid":{"rendered":"http:\/\/smart--grid.net\/?page_id=7157"},"modified":"2024-02-13T13:54:59","modified_gmt":"2024-02-13T12:54:59","slug":"algorithmes-neuronaux","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/","title":{"rendered":"Neural Algorithms 101"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"7157\" class=\"elementor elementor-7157\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-52ec29f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"52ec29f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-6924deb\" data-id=\"6924deb\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c9de6af elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"c9de6af\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/2020\/04\/03\/theories-and-algorithms-2\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Theories<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-e227679\" data-id=\"e227679\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b0af684 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"b0af684\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Home page<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ed8a315\" data-id=\"ed8a315\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4a581e4 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"4a581e4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/fr.wikipedia.org\/wiki\/R%C3%A9seau_de_neurones_artificiels\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7fd4506 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7fd4506\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-150fff3\" data-id=\"150fff3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c54b4e1 elementor-widget elementor-widget-toggle\" data-id=\"c54b4e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2061\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-2061\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">I. Perceptron and retropropagation (neural algorithms)<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2061\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-2061\"><ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/perceptron-en\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Perceptron (opens in a new tab)\">Perceptron<\/a>\n<ul>\n<li><a href=\"https:\/\/www.academia.edu\/19605060\/ADALINE_ADAptive_LInear_NEuron_Network\" target=\"_blank\" rel=\"noopener\">ADALINE<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.princeton.edu\/courses\/archive\/spring13\/cos511\/scribe_notes\/0411.pdf\" target=\"_blank\" rel=\"noopener\">Widrow-Hoff learning rules<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/retropagation\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opening in a new tab)\">Backpropagation<\/a>\n<ul>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/237127809_Accelerated_backpropagation_learning_Two_optimization_methods\" target=\"_blank\" rel=\"noopener\">Vogl method<\/a><\/li>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Artificial-neural-networks-theory-and-applications-Patterson\/fc97b0cf3fe34e41d9073f2da1c44daa7bcb036c\" target=\"_blank\" rel=\"noopener\">Delta-bar-delta<\/a><\/li>\n<li><a href=\"https:\/\/aip.scitation.org\/action\/cookieAbsent\" target=\"_blank\" rel=\"noopener\">Quickprop<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/298623\/\" target=\"_blank\" rel=\"noopener\">Rprop<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"http:\/\/web.mit.edu\/mcraegroup\/wwwfiles\/ChuangChuang\/thesis_files\/Appendix%20D_Artificial%20Neural%20Network.pdf\" target=\"_blank\" rel=\"noopener\">Hebb&#039;s rule<\/a><\/li>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/hopfield-network\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Hopfield Network (opens in a new tab)\">Hopfield Network<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/87054\/\" target=\"_blank\" rel=\"noopener\">Bidirectional associative memory<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2062\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-2062\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">II. Learning vector quantization<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2062\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-2062\"><ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/vector-quantization-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opening in a new tab)\">Learning vector quantization<\/a>\n<ul>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5726582\/\" target=\"_blank\" rel=\"noopener\">LVQ2<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5726582\/\" target=\"_blank\" rel=\"noopener\">LVQ2.1<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5726582\/\" target=\"_blank\" rel=\"noopener\">LVQ3<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5726582\/\" target=\"_blank\" rel=\"noopener\">OLVQ1<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/proceedings.neurips.cc\/paper\/1995\/file\/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf\" target=\"_blank\" rel=\"noopener\">GLVQ <\/a><\/li>\n<li><a href=\"https:\/\/dl.acm.org\/action\/cookieAbsent\" target=\"_blank\" rel=\"noopener\">Robust soft-LVQ<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.rug.nl\/~biehl\/Preprints\/mrslvqacc.pdf\" target=\"_blank\" rel=\"noopener\">Matrix RSLVQ<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-642-33212-8_2?error=cookies_not_supported&#038;code=bde83a4b-b635-485f-97af-0bda7ab8f4dd\" target=\"_blank\" rel=\"noopener\">Kernel RSLVQ<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11063-004-1547-1?error=cookies_not_supported&#038;code=18331ab1-bdbe-4781-ac64-6d9ab0a7d68c\" target=\"_blank\" rel=\"noopener\">Generalized relevance LVQ<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.rug.nl\/~biehl\/Preprints\/gmlvq.pdf\" target=\"_blank\" rel=\"noopener\">Generalized matrix LVQ<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1333849\/\" target=\"_blank\" rel=\"noopener\">Kernel GLVQ<\/a><\/li>\n<li><a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320304004108\" target=\"_blank\" rel=\"noopener\">Harmonic to minimum LVQ<\/a><\/li>\n<li><a href=\"https:\/\/direct.mit.edu\/neco\/article\/23\/5\/1343\/7660\/Divergence-Based-Vector-Quantization\" target=\"_blank\" rel=\"noopener\">Cauchyy-Schwarz Divergence LVQ<\/a><\/li>\n<li><a href=\"https:\/\/www.semanticscholar.org\/paper\/Efficient-Kernelized-Prototype-Based-Classification-Schleif-Villmann\/5069d2196cd60daed7b9e573783cec6bdc5e8770?p2df\" target=\"_blank\" rel=\"noopener\">Generalized LVQ nystrom-approximation<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2063\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-2063\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">III. Group method of data handling<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2063\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-2063\"><ul>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Group method of data handling<\/a>\n<ul>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Combinatorial (COMBI)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Multilayered Iterative (MIA)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">GN<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Objective System Analysis (OSA)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Harmonical<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Two-level (ARIMAD)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Multiplicative \u2013 Additive (MAA)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Objective Computer Clusterization (OCC);<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Pointing Finger (PF) clusterization algorithm;<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Analogs Complexing (AC)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Harmonical Rediscretization<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Algorithm on the base of Multilayered Theory of Statistical Decisions (MTSD)<\/a><\/li>\n<li><a href=\"https:\/\/gmdhsoftware.com\/GMDH_%20Anastasakis_and_Mort_2001.pdf\" target=\"_blank\" rel=\"noopener\">Group of Adaptive Models Evolution (GAME)<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2064\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-2064\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">IV. Self-organized map and neural gas<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2064\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-2064\"><ul>\n<li><a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/self-organized-card\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opening in a new tab)\">Self-organized card<\/a>\n<ul>\n<li><a href=\"https:\/\/www.academia.edu\/7552842\/The_Generative_Adaptive_Subspace_Self-Organizing_Map\" target=\"_blank\" rel=\"noopener\">ASSOM<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/mccormickml.com\/2013\/08\/15\/radial-basis-function-network-rbfn-tutorial\/\" target=\"_blank\" rel=\"noopener\">Network of radial basis functions<\/a><\/li>\n<li><a href=\"http:\/\/www.ks.uiuc.edu\/Publications\/Papers\/PDF\/MART91B\/MART91B.pdf\" target=\"_blank\" rel=\"noopener\">Neural gas<\/a>\n<ul>\n<li><a href=\"https:\/\/proceedings.neurips.cc\/paper\/1994\/file\/d56b9fc4b0f1be8871f5e1c40c0067e7-Paper.pdf\" target=\"_blank\" rel=\"noopener\">Growing NG<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11063-004-3255-2?error=cookies_not_supported&#038;code=f51bb663-a94b-46be-82bd-e5d755198a67\" target=\"_blank\" rel=\"noopener\">Supervised NG<\/a><\/li>\n<li><a href=\"https:\/\/repository.nida.ac.th\/bitstream\/handle\/662723737\/281\/nida-diss-b155731ab.pdf\" target=\"_blank\" rel=\"noopener\">Supervised growing NG<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"http:\/\/web.archive.org\/web\/20090206115723\/http:\/\/numenta.com\/Numenta_HTM_Concepts.pdf\" target=\"_blank\" rel=\"noopener\">Hierarchical temporal memory<\/a><\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2065\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-2065\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">V. Tutorials<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-2065\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-2065\">Back and forth content<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-402ba1cc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"402ba1cc\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-28d98f58\" data-id=\"28d98f58\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-356d1464 elementor-widget elementor-widget-text-editor\" data-id=\"356d1464\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/#Algorithmes-neuronaux\" >Neural algorithms<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Algorithmes-neuronaux\"><\/span>Neural algorithms<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">A biological neural network (where neural algorithms originate) refers to the information processing elements of the nervous system, organized as a collection of <a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/perceptron-en\/\">neuron cells<\/a>, called neurons, which are interconnected in networks and interact with each other using electrochemical signals. A biological neuron is usually composed of an axon which provides the input signals and is connected to other neurons via synapses. The neuron reacts to input signals and can produce an output signal on its output connection called the dendrites.<\/p>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">The field of artificial neural networks or algorithms (ANN) is concerned with the study of computer models inspired by theories and the observation of the structure and function of biological networks of neuronal cells in the brain. They are usually designed as models to solve problems <a href=\"https:\/\/complex-systems-ai.com\/en\/logic-math-27\/\">math<\/a>, IT and engineering. As such, there is a lot of interdisciplinary research in mathematics, neurobiology, and computer science.<\/p>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">An artificial neural network is usually composed of a collection of artificial neurons which are interconnected in order to perform certain calculations on input models and to create output models. They are adaptive systems capable of modifying their internal structure, usually the weights between network nodes, which allows them to be used for a variety of function approximation problems such as classification, <a href=\"https:\/\/complex-systems-ai.com\/en\/correlation-and-regressions\/\">regression<\/a>, feature extraction.<\/p>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">There are many types of neural networks, many of which fall into one of two categories:<\/p>\n<p><\/p>\n<p><\/p>\n<ul class=\"wp-block-list\">\n<li>Direct-acting networks: where input is provided on one side of the network and signals are propagated forward (in one direction) through the network structure on the other side where output signals are read. These networks can be made up of a cell, a layer or several layers of neurons. Some examples include the Perceptron, radial basis function arrays, and multilayer perceptron arrays.<\/li>\n<li>Recurring networks: where cycles in the network are allowed and the structure can be fully interconnected. Examples include network <a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/hopfield-network\/\">Hopfield<\/a> and bidirectional associative memory.<\/li>\n<\/ul>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">The artificial structures of the neural network are made up of nodes and weights that usually require training based on sample models of a problem area. Here are some examples of learning strategies:<\/p>\n<p><\/p>\n<p><\/p>\n<ul class=\"wp-block-list\">\n<li>Supervised learning: the network has a known expected response. The internal state of the network is modified to better match the expected result. Examples of this learning method include the algorithm of <a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/retropagation\/\">back propagation<\/a> and Hebb&#039;s rule.<\/li>\n<li>Unsupervised learning: the network is exposed to input patterns from which it must discern meaning and extract functionality. The most common type of unsupervised learning is competitive learning where neurons compete against each other based on the input pattern to produce an output pattern. Examples include neural gas, <a href=\"https:\/\/complex-systems-ai.com\/en\/neural-algorithms-2\/vector-quantization-learning\/\">vector quantization<\/a> of learning and the self-organizing map.<\/li>\n<\/ul>\n<p><\/p>\n<p><\/p>\n<p class=\"has-text-align-justify\">Artificial neural networks or algorithms are generally difficult to set up and slow to train, but once prepared they are very quick to apply. They are generally used for problem areas based on the approximation of functions and valued for their generalization and noise tolerance capabilities. They are known to be a black box, which means that it is difficult to explain the decisions made by the network.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-10160 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/1-s2.0-S0950705110000882-gr2.jpg\" alt=\"neural algorithms\" width=\"379\" height=\"285\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/1-s2.0-S0950705110000882-gr2.jpg 379w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/1-s2.0-S0950705110000882-gr2-300x226.jpg 300w\" sizes=\"(max-width: 379px) 100vw, 379px\" \/><\/p>\n<p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Theories Homepage Wiki I. Perceptron and backpropagation (neural algorithms) Perceptron ADALINE Widrow-Hoff learning rules Backpropagation Vogl&#039;s method Delta-bar-delta Quickprop Rprop Rule of \u2026 <\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7157","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7157","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/comments?post=7157"}],"version-history":[{"count":35,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7157\/revisions"}],"predecessor-version":[{"id":20679,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7157\/revisions\/20679"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/media?parent=7157"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}