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Amar Gupta. Leonida, Mike (Mike George), 1977- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2005-06-02T15:26:28Z 2005-06-02T15:26:28Z 2000 2000 http://hdl.handle.net/1721.1/17488 46818399 Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000. Includes bibliographical references (leaves 100-109). The research in this document is motivated by a problem which arises in the steel industry. The problem consists of predicting the temperature of a steel furnace based on the values of several inputs taken one through seven hours in advance (seven different sets of data). Two different time-delayed neural network (TDNN) implementations were used. The data was provided by a large steel plant located outside the United States. This work extends analysis already done by the group on this data using a multi-layer perceptron (MLP). This paper examines the architectures used in detail and then presents the results obtained. A survey of the data mining field related to TDNNs is also included. This survey consists of the theoretical background necessary to understand this kind of neural network, as well as recent progress and innovations involving TDNNs. Issues involved with running computationally intensive neural networks and the optimizations that have led to progress in this domain are also discussed. by Mike Leonida. M.Eng. 111 leaves 3290893 bytes 3290700 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. A time-delayed neural network approach to the prediction of the hot metal temperature in a blast furnace Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9f0e5d82-9e20-4a83-8601-e2e60ad8f05f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A time-delayed neural network approach to the prediction of the hot metal temperature in a blast furnace</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2000</PublicationDate> <Authors> <Author> <DisplayName>Leonida, Mike (Mike George), 1977-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The research in this document is motivated by a problem which arises in the steel industry. The problem consists of predicting the temperature of a steel furnace based on the values of several inputs taken one through seven hours in advance (seven different sets of data). Two different time-delayed neural network (TDNN) implementations were used. The data was provided by a large steel plant located outside the United States. This work extends analysis already done by the group on this data using a multi-layer perceptron (MLP). This paper examines the architectures used in detail and then presents the results obtained. A survey of the data mining field related to TDNNs is also included. This survey consists of the theoretical background necessary to understand this kind of neural network, as well as recent progress and innovations involving TDNNs. Issues involved with running computationally intensive neural networks and the optimizations that have led to progress in this domain are also discussed.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>