Abstract
An extension of T. Kohonen's (Biol. Cybern., vol. 43, pp. 59-69, 1982; vol. 44, pp. 135-140, 1982) self-organizing mapping algorithm together with an error-correction rule of the Widrow-Hoff type is applied to develop an unsupervised learning scheme for the visuo-motor coordination of a simulated robot arm. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a 3-D lattice between the units of the neural net.
| Original language | English (US) |
|---|---|
| Title of host publication | IJCNN Int Jt Conf Neural Network |
| Editors | Anon |
| Publisher | Publ by IEEE |
| Pages | 351-356 |
| Number of pages | 6 |
| State | Published - 1989 |
| Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: Jun 18 1989 → Jun 22 1989 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
|---|---|
| City | Washington, DC, USA |
| Period | 6/18/89 → 6/22/89 |
ASJC Scopus subject areas
- General Engineering