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.
|Title of host publication||Computational Intelligent Systems for Applied Research, Proceedings of the 5th International FLINS Conference|
|Editors||Da Ruan, Pierre D'hondt, Etienne E. Kerre|
|Place of Publication||Singapore|
|Number of pages||10|
|Publication status||Published - Sep 2002|