Process identification through modular neural networks and rule extraction


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
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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