Molding the Knowledge in Modular Neural Networks

Spaanenburg, L. and Achterop, S. and Slump, C.H. and Zwaag van der, B.J. (2002) Molding the Knowledge in Modular Neural Networks. In: Lerende Oplossingen. Technologiestichting STW, Nijmegen, The Netherlands, pp. 25-26.

Preview

PDF
14Kb

Abstract:	Problem description. The learning of monolithic neural networks becomes harder with growing network size. Likewise the knowledge obtained while learning becomes harder to extract. Such disadvantages are caused by a lack of internal structure, that by its presence would reduce the degrees of freedom in evolving to a training target. A suitable internal structure with respect to modular network construction as well as to nodal discrimination is required. Details on the grouping and selection of nodes can sometimes be concluded from the characteristics of the application area; otherwise a comprehensive search within the solution space is necessary.
Item Type:	Book Section
Copyright:	©STW Technology Foundation
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Signals and Systems (SAS)
Link to this item:	http://purl.utwente.nl/publications/43372
Export this item as:	BibTeX EndNote HTML Citation Reference Manager

Show download statistics for this publication

Repository Staff Only: item control page

Metis ID: 206439