Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "12540"
#_source: array:26 [
"id" => "12540"
"slug" => "bayesian-inference-in-non-markovian-state-space-models-with-applications-to-battery-fractional-order-systems"
"yearMonth" => "2018-03"
"year" => "2018"
"title" => "Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems"
"description" => "JACOB, P., ALAVI, S.M.M., MAHDI, A., PAYNE, S.J. et HOWEY, D.A. (2018). Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems. <i>IEEE Transactions on Control Systems Technology</i>, 26(2), pp. 497-506."
"authors" => array:5 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:1 [
"name" => "ALAVI Seyed Mohammad Mahdi"
]
2 => array:1 [
"name" => "MAHDI Adam"
]
3 => array:1 [
"name" => "PAYNE Stephen J."
]
4 => array:1 [
"name" => "HOWEY David A."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://ieeexplore.ieee.org/document/7873246"
"publicationInfo" => array:3 [
"pages" => "497-506"
"volume" => "26"
"number" => "2"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting."
"en" => "Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-22T01:21:45.000Z"
"docTitle" => "Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, ALAVI Seyed Mohammad Mahdi, MAHDI Adam, PAYNE Stephen J., HOWEY David A."
"docDescription" => "<span class="document-property-authors">JACOB Pierre, ALAVI Seyed Mohammad Mahdi, MAHDI Adam, PAYNE Stephen J., HOWEY David A.</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2018</span>"
"keywordList" => ""
"docPreview" => "<b>Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems</b><br><span>2018-03 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://ieeexplore.ieee.org/document/7873246" target="_blank">Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.957596
+"parent": null
}