Abstract
In contrast to the usual types of neural networks which utilize two states for each neuron, a class of synchronous discrete-time multilevel threshold neural networks is developed. A qualitative analysis and a synthesis procedure of the class of neural networks constitute the principal contributions of this work. The applicability of the class of neural networks is demonstrated by means of a gray-level image processing example in which each neuron of the present model assumes one of 16 values. In doing so, the number of neurons and the number of interconnections are reduced, when compared to the usual binary state networks.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
| Publisher | Publ by IEEE |
| Pages | 1461-1464 |
| Number of pages | 4 |
| Volume | 3 |
| State | Published - 1991 |
| Externally published | Yes |
| Event | 1991 IEEE International Symposium on Circuits and Systems Part 4 (of 5) - Singapore, Singapore Duration: Jun 11 1991 → Jun 14 1991 |
Other
| Other | 1991 IEEE International Symposium on Circuits and Systems Part 4 (of 5) |
|---|---|
| City | Singapore, Singapore |
| Period | 6/11/91 → 6/14/91 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials