Process Identification through modular neural networks and rule extraction
(2002) 5th International Conference on Computational Intelligent Systems for Applied Research (FLINS) p.268-277- 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
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/603905
- author
- vanderZwaag, B J ; Slump, C H and Spaanenburg, Lambert LU
- publishing date
- 2002
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Neural Networks, Modularity, Functional Base, Process Model, Rule Extraction
- host publication
- Proceedings FLINS 2002
- pages
- 268 - 277
- conference name
- 5th International Conference on Computational Intelligent Systems for Applied Research (FLINS)
- conference location
- Gent, Belgium
- conference dates
- 2002-09-16 - 2002-09-18
- language
- English
- LU publication?
- no
- id
- a7fb62ac-a8cf-4f96-a6c8-ccfc0c1da9fc (old id 603905)
- date added to LUP
- 2016-04-04 13:22:38
- date last changed
- 2018-11-21 21:13:33
@inproceedings{a7fb62ac-a8cf-4f96-a6c8-ccfc0c1da9fc, 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 Spaanenburg, Lambert}}, booktitle = {{Proceedings FLINS 2002}}, keywords = {{Neural Networks; Modularity; Functional Base; Process Model; Rule Extraction}}, language = {{eng}}, pages = {{268--277}}, title = {{Process Identification through modular neural networks and rule extraction}}, year = {{2002}}, }