Sammanfattning
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.
Originalspråk | engelska |
---|---|
Titel på värdpublikation | Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02) |
Sidor | 507-508 |
Status | Published - 2002 |
Externt publicerad | Ja |
Evenemang | Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002 - Leuven, Belgien Varaktighet: 2002 okt. 21 → 2002 okt. 22 |
Publikationsserier
Namn | |
---|---|
ISSN (tryckt) | 1568-7805 |
Konferens
Konferens | Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002 |
---|---|
Land/Territorium | Belgien |
Ort | Leuven |
Period | 2002/10/21 → 2002/10/22 |
Ämnesklassifikation (UKÄ)
- Elektroteknik och elektronik