Much research in recent years has been done in applying artificial neural networks to the problem of nonlinear system identification. The most common neural network architecture, the multilayer feed-forward network, trained with the backpropagation algorithm, has been shown to be capable of universal function approximation which makes it applicable to a much wider range of problems than other nonlinear identification techniques. While these neural networks show great potential, they still suffer several drawbacks, such as slow convergence toward a solution. New neural network architectures have been proposed in an attempt to overcome these limitations. This study examines one such architecture, Cascade-Correlation, and its usefulness in system identification applications, particularly the nonlinear case. / M.S.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/112536 |
| Date | January 1994 |
| Creators | Mueller, Klaus C. |
| Contributors | Electrical Engineering |
| Publisher | Virginia Polytechnic Institute and State University |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | vi, 114 leaves, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 32290680 |
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