Computationally complex, nonlinear systems modeling using neural networks
"Neural networks, a powerful machine learning paradigm, have been successfully applied to a wide spectrum of practical problems. This dissertation discusses the modeling of three nonlinear systems using neural networks. Although they are different problems in different fields, they share a common factor - they are all complex nonlinear systems and they all utilize neural networks to model the system and to solve the problem"--Abstract, leaf iv.
Electrical and Computer Engineering
Ph. D. in Computer Engineering
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Neural network inverse model applications in aircraft engine balancing
- General recurrent neural network approach to model genetic regulatory networks
- Time series prediction with a weighted bidirectional multi-stream extended Kalman filter
xi, 74 leaves
© 2004 Xiao Hu, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Neural networks (Computer science) -- Mathematical models
Nonlinear systems -- Mathematical models
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5369389~S5
Hu, Xiao, "Computationally complex, nonlinear systems modeling using neural networks" (2004). Doctoral Dissertations. 1555.