<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
		<Article>
		<Journal>
			<PublisherName>Majlesi Journal of Electrical Engineering</PublisherName>
			<JournalTitle>Short-term Prediction of Traffic Rate Interval Router Using Dynamic Synapse Neural Network</JournalTitle>
			<Issn></Issn>
			<Volume>Volume 3 (2009)</Volume>
			<Issue>Issue 2, March 2009</Issue>
			<PubDate PubStatus="epublish">
                <Year>2024</Year>
                <Month>02</Month>
                <Day>28</Day>
			</PubDate>
		</Journal>
		<ArticleTitle>Short-term Prediction of Traffic Rate Interval Router Using Dynamic Synapse Neural Network</ArticleTitle>
		<VernacularTitle></VernacularTitle>
		<FirstPage></FirstPage>
		<LastPage></LastPage>
		<ELocationID EIdType="doi">10.1234/mjee.v3i2.286</ELocationID>
		<Language>EN</Language>
		<AuthorList>
            			<Author>
                				<FirstName>Maryam</FirstName>
				<LastName>Shakiba</LastName>
				<Affiliation></Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            			<Author>
                				<FirstName>Mohamad</FirstName>
				<LastName>Teshnelab</LastName>
				<Affiliation></Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            			<Author>
                				<FirstName>Sadan</FirstName>
				<LastName>Zokaei</LastName>
				<Affiliation></Affiliation>
				<Identifier Source="ORCID"></Identifier>
			</Author>
            		</AuthorList>
		<PublicationType>Journal Article</PublicationType>
		<History>
			<PubDate PubStatus="received">
				<Year>2024</Year>
				<Month>02</Month>
				<Day>28</Day>
			</PubDate>
		</History>
		<Abstract>Prediction is an important issue in many dynamical systems and is vital for effective management and control of plants. An important process which has recently derived much attention is the congestion control problem. Prediction of different traffic parameters can help in managing a congestion in a computer network. In this thesis, using real data for from the router between Iran Telecommunication Research Center and data Data company during December, January, February and March 2007, the router interval traffic rates are analyzed. Also, a comparative study is performed using the different methods employed and prediction results are provided to show the effectiveness of the predictions.</Abstract>
		<ObjectList>
            			<Object Type="keyword">
				<Param Name="value">Prediction</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">PV panel</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Solar output</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">GUI</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">ANN</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Prediction of Time Series</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Time Delay Line Neural Network</Param>
			</Object>
						<Object Type="keyword">
				<Param Name="value">Dynamic Synapse Neural Network and PSO Algorithm</Param>
			</Object>
					</ObjectList>
	</Article>
	</ArticleSet>
