English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30551
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

An algebraic framework to represent finite state machines in single-layer recurrent neural networks

AuthorsAlquézar Mancho, Renato ; Sanfeliu, Alberto
KeywordsPattern recognition
Issue Date1995
PublisherMassachusetts Institute of Technology
CitationNeural Computation 7(5): 931-949 (1995)
AbstractIn this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).
Publisher version (URL)http://dx.doi.org/10.1162/neco.1995.7.5.931
URIhttp://hdl.handle.net/10261/30551
DOI10.1162/neco.1995.7.5.931
ISSN0899-7667
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
algebraic framework.pdf303,1 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.