Process Identification through modular neural networks and rule extraction

B J vanderZwaag; C H Slump; Lambert Spaanenburg

Process Identification through modular neural networks and rule extraction

B J vanderZwaag, C H Slump, Lambert Spaanenburg

External Organization - Unknown

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

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.

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

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@inproceedings{b15af59052674196a3bf0812a0694016,

title = "Process Identification through modular neural networks and rule extraction",

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.",

author = "vanderZwaag, \{B J\} and Slump, \{C H\} and Lambert Spaanenburg",

year = "2002",

language = "English",

pages = "507--508",

booktitle = "Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)",

note = "Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002 ; Conference date: 21-10-2002 Through 22-10-2002",

}

TY - GEN

T1 - Process Identification through modular neural networks and rule extraction

AU - vanderZwaag, B J

AU - Slump, C H

AU - Spaanenburg, Lambert

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

M3 - Paper in conference proceeding

SP - 507

EP - 508

BT - Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)

T2 - Belgium/Netherlands Conference on Artificial Intelligence (BNAIC), 2002

Y2 - 21 October 2002 through 22 October 2002

ER -

Process Identification through modular neural networks and rule extraction

Sammanfattning

Publikationsserier

Konferens

Ämnesklassifikation (UKÄ)

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