A time-delayed neural network approach to the prediction of the hot metal temperature in a blast furnace
Name
46818399-MIT.pdf
Description
Full printable version
Size
4.84 MB
Format
Adobe PDF
Checksum (MD5)
f8a237de7188f3c84ad19997dc7168b3
Author(s)
Leonida, Mike (Mike George), 1977-
Advisor(s)
Amar Gupta.
Date Issued
2000
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 100-109).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
