Computationally complex, nonlinear systems modeling using neural networks

Author

Xiao Hu

Abstract

"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, page iv.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Publisher

University of Missouri--Rolla

Publication Date

Fall 2004

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

Pagination

xi, 74 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2004 Xiao Hu, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Neural networks (Computer science) -- Mathematical models
Kalman filtering
Nonlinear systems -- Mathematical models

Thesis Number

T 8642

Print OCLC #

61853374

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Hu, Xiao, "Computationally complex, nonlinear systems modeling using neural networks" (2004). Doctoral Dissertations. 1555.
https://scholarsmine.mst.edu/doctoral_dissertations/1555