Doctoral Dissertations

Advanced architecture and training algorithms for recurrent neural networks


Xindi Cai


"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.


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-


Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering


Boeing Company. Boeing Phantom Works
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)


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


x, 76 pages

Note about bibliography

Includes bibliographical references.


© 2006 Xindi Cai, All rights reserved.

Document Type

Dissertation - Citation

File Type




Subject Headings

Back propagation (Artificial intelligence)
Evolutionary programming (Computer science)
Kalman filtering
Mathematical optimization
Neural networks (Computer science)
Swarm intelligence

Thesis Number

T 8996

Print OCLC #


Link to Catalog Record

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