Process identification through modular neural networks and rule extraction

Zwaag van der, B.J. and Slump, C.H. and Spaanenburg, L. (2002) Process identification through modular neural networks and rule extraction. In: 14th Dutch-Belgian Artificial Intelligence Conference, BNAIC, 21-22 October 2002, Leuven, Belgium.

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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. [Full paper published as: Berend Jan van der Zwaag, Kees Slump, and Lambert Spaanenburg. Process identification through modular neural networks and rule extraction. In Proceedings FLINS-2002, Ghent, Belgium, 16-18 Sept. 2002.]
Item Type:	Conference or Workshop Item
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Signals and Systems (SAS)
Link to this item:	http://purl.utwente.nl/publications/43784
Conference URL:	http://www.cs.kuleuven.be/conference/bnaic02/
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