Neural networks are models which have been developed to simulate the anatomy
of the nervous system. The connection between the elements of these networks,
the so called artificial neurons, is similar to the connection between the biological
neurons. In developing neural networks people are trying to create systems which
have the same computational and communication properties as the brain.
On the basis of the things we know from neurophysiology the first models for the
neural networks are developed. One of these networks was the perceptron, which is
one of the most used neural networks. In this thesis we'll study this special neural
network. When the input vectors of the perceptron can be linearly separated into
two categories, this network can be trained to correctly classify these input vectors.
However in most practical cases the linearly separability assumption isn't satisfied.
That's why the main part of this study is devoted to the case where the input vectors
aren't linearly separable. / Graduation date: 1995
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/35557 |
| Date | 26 May 1994 |
| Creators | Venema, Rienk S. |
| Contributors | Burton, Robert M. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0018 seconds