Abstract
Monolithic neural networks may be trained from measured data to establish knowledge
about the process. Unfortunately, this knowledge is not guaranteed to be found
and – if at all – hard to extract. Modular neural networks are better suited for this
purpose. Domain-ordered by topology, rule extraction is performed module by module.
This has all the benefits of a divide-and-conquer method and opens the way to
structured design. This paper discusses a next step in this direction by illustrating the
potential of base functions to design the neural model.
about the process. Unfortunately, this knowledge is not guaranteed to be found
and – if at all – hard to extract. Modular neural networks are better suited for this
purpose. Domain-ordered by topology, rule extraction is performed module by module.
This has all the benefits of a divide-and-conquer method and opens the way to
structured design. This paper discusses a next step in this direction by illustrating the
potential of base functions to design the neural model.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02) |
| Pages | 507-508 |
| Publication status | Published - 2002 |
| Externally published | Yes |
| Event | Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002 - Leuven, Belgium Duration: 2002 Oct 21 → 2002 Oct 22 |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1568-7805 |
Conference
| Conference | Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002 |
|---|---|
| Country/Territory | Belgium |
| City | Leuven |
| Period | 2002/10/21 → 2002/10/22 |
Subject classification (UKÄ)
- Electrical Engineering, Electronic Engineering, Information Engineering