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<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Tabriz</PublisherName>
				<JournalTitle>Computational Methods for Differential Equations</JournalTitle>
				<Issn>2345-3982</Issn>
				<Volume>6</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2018</Year>
					<Month>01</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Numerical treatment for nonlinear steady flow of a third grade‎ fluid in a porous half space by neural networks optimized</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>53</FirstPage>
			<LastPage>62</LastPage>
			<ELocationID EIdType="pii">6815</ELocationID>
			
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Mohsen</FirstName>
					<LastName>Alipour</LastName>
<Affiliation>Department of Mathematics, Babol Noshirvani University of Technology, Shariati Ave.,
Babol, Iran, Post Code: 47148-71167</Affiliation>

</Author>
<Author>
					<FirstName>Kobra</FirstName>
					<LastName>Karimi</LastName>
<Affiliation>Deprtment of Mathematics, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>05</Month>
					<Day>11</Day>
				</PubDate>
			</History>
		<Abstract>In this paper‎, ‎steady flow of a third-grade fluid in a porous half‎ space has been considered‎. ‎This problem is a nonlinear two-point‎ boundary value problem (BVP) on semi-infinite interval‎. ‎The‎ solution for this problem is given by a numerical method based on the feed-forward artificial‎ neural network model using radial basis activation functions trained with an interior point method‎. ‎Moreover, to confirm the performance of the proposed technique‎, ‎our results are compared with other available  ‎results‎. ‎Numerical results demonstrate the validity and‎ ‎applicability of the ‎technique.‎</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Feed forward neural network‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Radial basis functions‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Semi infinite‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Steady flow‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">‎Third-grade ‎fluid</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://cmde.tabrizu.ac.ir/article_6815_e120001746c4d99862eb7e2964d70abc.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
