Article(id=1242119412812157894, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242119397003826032, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1255363200000, receivedDateStr=2009-10-13, revisedDate=1259683200000, revisedDateStr=2009-12-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1263312000000, onlineDateStr=2010-01-13, pubDate=1263312000000, pubDateStr=2010-01-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1263312000000, onlineIssueDateStr=2010-01-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073945473, creator=sys-migrate, updateTime=1774073945473, updator=sys-migrate, issue=Issue{id=1242119397003826032, tenantId=1146029695717560320, journalId=1146031591421210625, year='2010', volume='28', issue='1001', pageStart='1', pageEnd='139', issueExtLink='null', onlineDate='null', pubDate='1263312000000', pubDateStr='2010-01-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073941703, creator='sys-migrate', updateTime=1774073941703, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=74, endPage=77, ext={EN=ArticleExt(id=1242119416834495450, articleId=1242119412812157894, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=APPLICATION OF ARTIFICIAL FISH-SWARM NEURAL NETWORK IN COILING TEMPERATURE FORECASTING OF HOT ROLLED STRIP, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=The coiling temperature forecasting, as a non-linear optimal problem, is very important to the performance of hot rolled strip products. Based on the individual local searching, the Artificial Fish-swarm Algorithm(AFSA) is a new optimal strategy, with good capability to avoid the local extremum and obtain the global extremum. In this paper, an Artificial Neural Network(ANN) based the forecasting model of AFSA is proposed, with the weights being trained by AFSA, and the neural network of AFSA being applied to coiling temperature forecasting. Applying the forecasting method to a certain actual hot rolled strip, it is shown that comparing with the traditional BP neural network forecasting method, the presented forecasting method has better adaptive ability and can give better forecasting results. The artificial fish-swarm algorithm network is trained and checked with the actual production data. The result indicates that the method can predict the strip coiling temperature in real-time., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=/LFX5NWuepWjZ/2vgBJ8SQ==, pdfFileSize=632444, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242119416033383381, articleId=1242119412812157894, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=人工鱼群神经网络在热连轧卷取温度预报中的应用, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=卷取温度预报结果对热轧带钢的成品性能具有重要影响。人工鱼群算法是新近提出的寻优策略，具有良好的克服局部极值、获得全局极值的能力。建立了一种人工鱼群神经网络预测模型，利用人工鱼群算法训练神经网络的权值，并将该神经网络用于卷取温度预报。通过某钢厂现场实测数据对该模型进行离线训练和对比测试，结果表明，该方法与传统的BP神经网络预测方法相比，具有较强的自适应能力和较好的预报效果；该模型能够精确预报卷取温度，可用于离线学习和预报，为在线应用打下良好基础。, authors=郭强,张超,莫天生, authorsList=郭强;张超;莫天生, authorCompany=北京科技大学高效轧制国家工程研究中心，北京 100083, correspAuthors=null, authorNote=null, correspAuthorsNote=张超, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=DU4rohRF9qhvizGMrlZAjg==, pdfFileSize=632444, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, keywords=[Keyword(id=1242119414376629022, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242119412812157894, language=CN, orderNo=1, keyword=人工鱼群), 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