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.
|Conference||5th International FLINS Conference Computational Intelligent Systems for Applied Research|
|Period||16/09/02 → 18/09/02|
|Other||September 16-18, 2002|