Keywords
relaxation network, probabilistic connections, input persistence, activation function
Abstract
This paper reports results from studying the behavior of Hopfield-type networks with probabilistic connections. As the probabilities decrease, network performance degrades. In order to compensate, two network modifications - input persistence and a new activation function - are suggested, and empirical results indicate that the modifications significantly improve network performance.
Original Publication Citation
Dan Ventura, "Probabilistic Connections in Relaxation Networks", Proceedings of the International Joint Conference on Neural Networks, pp.934-938, May 22.
BYU ScholarsArchive Citation
Ventura, Dan A., "Probabilistic Connections in Relaxation Networks" (2002). Faculty Publications. 543.
https://scholarsarchive.byu.edu/facpub/543
Document Type
Peer-Reviewed Article
Publication Date
2002-05-01
Permanent URL
http://hdl.lib.byu.edu/1877/2532
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/