Abstract
The practical aspects of identifying a nonlinear multi-input multi-output dynamic system using feed-forward neural networks (NN) is the subject of this paper. By utilizing the measurements of 25 input and internal variables of the process, the primary process output is estimated with a network that has one hidden layer and partial connectivity. Two different connectivity patterns are compared, and problems encountered during the development are summarized. The accuracy of the estimate is demonstrated by comparing the NN output with the process output in time domain and frequency domain.
| Original language | English (US) |
|---|---|
| Title of host publication | 1993 IEEE International Conference on Neural Networks |
| Editors | Anon |
| Publisher | IEEE Computer Society |
| Pages | 564-567 |
| Number of pages | 4 |
| ISBN (Print) | 0780312007 |
| State | Published - 1993 |
| Event | 1993 IEEE International Conference on Neural Networks - San Francisco, CA, USA Duration: Mar 28 1993 → Apr 1 1993 |
Other
| Other | 1993 IEEE International Conference on Neural Networks |
|---|---|
| City | San Francisco, CA, USA |
| Period | 3/28/93 → 4/1/93 |
ASJC Scopus subject areas
- General Engineering