Doctoral Dissertations
Advanced architecture and training algorithms for recurrent neural networks
Abstract
"Recurrent neural networks (RNN) attract considerable interest in computational intelligence fields because of their superior power in processing spatio-temporal data and time-varying signals. Traditionally, the recurrency of a neural network occurs between input samples along the time axis. The simultaneous recurrent network (SRN) extends the recurrent property to the spatial dimension. Presenting the feedback information with the same input vector to the network illustrates the transient properties of the system, which helps to trace error propagation and facilitates training. Backpropagation through time and extended Kalman filter are suitable gradient-based training algorithms for RNN"--Abstract, page iv.
Advisor(s)
Wunsch, Donald C.
Committee Member(s)
Beetner, Daryl G.
Venayagamoorthy, Ganesh K.
Prokhorov, Danil V.
Dagli, Cihan H., 1949-
Vian, John L.
Sarangapani, Jagannathan, 1965-
Department(s)
Electrical and Computer Engineering
Degree Name
Ph. D. in Electrical Engineering
Sponsor(s)
Boeing Company. Boeing Phantom Works
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Publisher
University of Missouri--Rolla
Publication Date
Spring 2006
Journal article titles appearing in thesis/dissertation
- Training winner-take-all simultaneous recurrent neural networks
- Evolutionary swarm neural network game engine for Capture Go
- Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm
Pagination
x, 76 pages
Note about bibliography
Includes bibliographical references.
Rights
© 2006 Xindi Cai, All rights reserved.
Document Type
Dissertation - Citation
File Type
text
Language
English
Subject Headings
Back propagation (Artificial intelligence)Evolutionary programming (Computer science)Kalman filteringMathematical optimizationNeural networks (Computer science)Swarm intelligence
Thesis Number
T 8996
Print OCLC #
123131001
Recommended Citation
Cai, Xindi, "Advanced architecture and training algorithms for recurrent neural networks" (2006). Doctoral Dissertations. 1686.
https://scholarsmine.mst.edu/doctoral_dissertations/1686
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