Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "4620"
  #_source: array:26 [
    "id" => "4620"
    "slug" => "fixed-points-of-the-abe-formulation-of-stochastic-hopfield-networks"
    "yearMonth" => "2007-09"
    "year" => "2007"
    "title" => "Fixed Points of the Abe Formulation of Stochastic Hopfield Networks"
    "description" => "KRATZ, M., ATENCIA, M. et JOYA, G. (2007). Fixed Points of the Abe Formulation of Stochastic Hopfield Networks. Dans: <i>ICANN - LNCS 4668</i>. Springer."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "KRATZ Marie"
        "bid" => "B00072305"
        "slug" => "kratz-marie"
      ]
      1 => array:1 [
        "name" => "ATENCIA M."
      ]
      2 => array:1 [
        "name" => "JOYA G."
      ]
    ]
    "ouvrage" => "ICANN - LNCS 4668"
    "keywords" => array:1 [
      0 => "Stochastic Hopfield Neural Networks"
    ]
    "updatedAt" => "2021-04-19 17:57:25"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "The stability of stochastic Hopfield neural networks, in the Abe formulation, is studied. The aim is to determine whether the ability of the deterministic system to solve combinatorial optimization problems is preserved after the addition of random noise. In particular, the stochastic stability of the attractor set is analyzed: vertices, which are feasible points of the problem, should be stable, whereas interior points, which are unfeasible, should be unstable. Conditions on the noise intensity are stated, so that these properties are guaranteed. This theoretical investigation establishes the foundations for practical application of stochastic networks to combinatorial optimization."
      "en" => "The stability of stochastic Hopfield neural networks, in the Abe formulation, is studied. The aim is to determine whether the ability of the deterministic system to solve combinatorial optimization problems is preserved after the addition of random noise. In particular, the stochastic stability of the attractor set is analyzed: vertices, which are feasible points of the problem, should be stable, whereas interior points, which are unfeasible, should be unstable. Conditions on the noise intensity are stated, so that these properties are guaranteed. This theoretical investigation establishes the foundations for practical application of stochastic networks to combinatorial optimization."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T08:21:48.000Z"
    "docTitle" => "Fixed Points of the Abe Formulation of Stochastic Hopfield Networks"
    "docSurtitle" => "Actes d'une conférence"
    "authorNames" => "<a href="/cv/kratz-marie">KRATZ Marie</a>, ATENCIA M., JOYA G."
    "docDescription" => "<span class="document-property-authors">KRATZ Marie, ATENCIA M., JOYA G.</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2007</span>"
    "keywordList" => "<a href="#">Stochastic Hopfield Neural Networks</a>"
    "docPreview" => "<b>Fixed Points of the Abe Formulation of Stochastic Hopfield Networks</b><br><span>2007-09 | Actes d'une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Fixed Points of the Abe Formulation of Stochastic Hopfield Networks</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.277121
  +"parent": null
}