Process identification through modular neural networks and rule extraction


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Zwaag, Berend Jan van der and Slump, Kees and Spaanenburg, Lambert (2002) Process identification through modular neural networks and rule extraction. In: 5th International FLINS Conference Computational Intelligent Systems for Applied Research, September 16-18, 2002, Gent, Belgium (pp. pp. 268-277).

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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.
Item Type:Conference or Workshop Item
Copyright:© World Scientific
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Electrical Engineering, Mathematics and Computer Science (EEMCS)
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Link to this item:http://purl.utwente.nl/publications/43692
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