Fractional order integro-differential equations solution by artificial neural networks approach

Jafarian Ahmad; Measoomy Nia Safa

Articolul precedent

Articolul urmator

233

Ultima descărcare din IBN:
2023-10-15 11:28

SM ISO690:2012

JAFARIAN, Ahmad, MEASOOMY NIA, Safa. Fractional order integro-differential equations solution by artificial neural networks approach. In: Conference on Applied and Industrial Mathematics: CAIM 2017, 14-17 septembrie 2017, Iași. Chișinău: Casa Editorial-Poligrafică „Bons Offices”, 2017, Ediţia 25, p. 32. ISBN 978-9975-76-247-2.

EXPORT metadate:
Google Scholar
Crossref
CERIF

DataCite
Dublin Core

Conference on Applied and Industrial Mathematics
Ediţia 25, 2017

Conferința "Conference on Applied and Industrial Mathematics"
Iași, Romania, 14-17 septembrie 2017

Fractional order integro-differential equations solution by artificial neural networks approach

Pag. 32-32

Jafarian Ahmad, Measoomy Nia Safa

Islamic Azad University, Urmia

Disponibil în IBN: 23 septembrie 2022

Descarcă PDF

Rezumat

Great care must be taken in considering the fact that neural networks moved in the direction of a systematic world such as applied mathematics and engineering sciences. Such certain movement helped shaping fantastic changes in the numerical solution of complicated cases which are overt in natural phenomena. In the present study, a comprehensive optimization mechanism consisting of a reliable three-layered feed-forward neural network is formed to solve a class of fractional order ordinary integro-di erential equations. One point should be kept in mind that the supervised backpropagation type learning algorithm which is based on the gradient descent method, is capable of approximating the mentioned problem on an arbitrary interval to any desired degree of accuracy. Besides, some comparative test problems are given to reveal the exibility and eciency of the proposed method.

Cuvinte-cheie
Fractional order integro-differential equation, Artificial neural networks approach, Least mean squares cost function, Supervised back-propagation learning algorithm

Crossref XML Export

<?xml version='1.0' encoding='utf-8'?>
<doi_batch version='4.3.7' xmlns='http://www.crossref.org/schema/4.3.7' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xsi:schemaLocation='http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd'>
<head>
<doi_batch_id>ibn-164352</doi_batch_id>
<timestamp>1718821314</timestamp>
<depositor>
<depositor_name>Information Society Development Instiute, Republic of Moldova</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
</head>
<body>
<collection>
<collection_metadata>
<full_title>Conference on Applied and Industrial Mathematics</full_title>
</collection_metadata>
<collection_issue>
<publication_date media_type='print'>
<year>2017</year>
</publication_date>
<isbn> 978-9975-76-247-2</isbn>
</collection_issue>
<collection_article publication_type='full_text'><titles>
<title>Fractional order integro-differential equations solution by artificial neural networks approach</title>
</titles>
<contributors>
<person_name sequence='first' contributor_role='author'>
<given_name>Ahmad</given_name>
<surname>Jafarian</surname>
</person_name>
<person_name sequence='additional' contributor_role='author'>
<given_name>Safa</given_name>
<surname>Measoomy Nia</surname>
</person_name>
</contributors>
<publication_date media_type='print'>
<year>2017</year>
</publication_date>
<pages>
<first_page>32</first_page>
<last_page>32</last_page>
</pages>
</collection_article>
</collection>
</body>
</doi_batch>