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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ramacher, U. Wesseling, M. |
| Copyright Year | 1989 |
| Description | Author affiliation: Siemens AG, Munich, West Germany (Ramacher, U.; Wesseling, M.) |
| Abstract | A geometrical method for deriving the topology of a neural net is proposed. Instead of making use of learning algorithms, the pattern space is analyzed. The method is outlined for a neural net which performs a binary representation of an analog sensory input. This leads to a geometrical determination of the structure of the neural net, i.e. its layers, weights, and thresholds; no learning is necessary. It is shown that the number of layers and neurons in a feedforward MLP specialized to A/D conversion can be reduced considerably by introducing feedback. The geometrical approach turns out to provide various alternatives for the design of such a net. The results obtained strongly support the view that geometrical analysis of the pattern structure to be recognized helps avoid unnecessary learning. |
| Starting Page | 147 |
| Ending Page | 153 |
| File Size | 393884 |
| Page Count | 7 |
| File Format | |
| DOI | 10.1109/IJCNN.1989.118692 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1989-01-01 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational geometry Circuit topology Neural networks Parallel architectures |
| Content Type | Text |
| Resource Type | Article |
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