This dissertation studies neural networks for pattern classification and universal approximation. The objective is to develope a new neural network model for pattern classification, and relax the conditions for Radial-Basis Function networks to be universal approximators. First, the problem of pattern classification is introduced, which is followed by a brief introduction of three popular nonlinear classification techniques, that is, Multi-Layer Perceptrons (MLP), Radial-Basis Function (RBF) networks, and Support Vector Machines (SVM). Then, based on the basic concepts of MLP, RBF and SVM, a new neural network model with bounded weights is proposed, and some experimental results are reported. Later, the problem of universal approximation by neural networks is introduced, and the researches on ridge activation functions and radial-basis activation functions are reviewed. Then, the relaxed conditions for RBF networks to be universal approximators are presented. We show that RVF networks can uniformly approximate any continuous function on a compact set provided that the radial basis activation function is continuous almost every where, locally essentially bounded, and not a polynomial. Some experimental results are reported to illustrate our findings. The dissertation ends with the conclusion and future research.
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-04252002-141320 |
| Date | 08 July 2002 |
| Creators | Liao, Yi |
| Contributors | Shu-Cherng Fang, Henry L. W. Nuttle, Yuan-Shin Lee, Jesus Rodriguez |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-04252002-141320/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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