Affordable Access

Process identification through modular neural networks and rule extraction

Authors
Publication Date
Disciplines
  • Computer Science
  • Design
  • Mathematics

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.]

There are no comments yet on this publication. Be the first to share your thoughts.