DocumentCode
1817154
Title
Tools for dynamic network structures: GRAPE grammars
Author
Freund, Rudolf ; Haberstroh, Brigitte ; Bischof, Horst
Author_Institution
Tech. Univ. of Vienna, Austria
Volume
1
fYear
1992
fDate
7-11 Jun 1992
Firstpage
737
Abstract
A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology learning algorithms is proposed. It is shown how GRAPE grammars can be used to model neural networks. Special emphasis is placed on topology learning. It is concluded that GRAPE grammars offer a great potential for neural networks, giving the possibility of a common language suited for all kinds of neural networks
Keywords
grammars; learning (artificial intelligence); neural nets; GRAPE grammars; attributed elementary programmed graph grammars; dynamic network structures; network dynamics; neural networks; topology learning algorithms; weight learning algorithms; Computer languages; Dynamic programming; Electronic mail; Formal languages; Heuristic algorithms; Network topology; Neural networks; Pattern recognition; Pipelines; Production;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.287099
Filename
287099
Link To Document