A Tabu Based Neural Network Training Algorithm for Equalization of Communication Channels

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/877

Full metadata record

DC Field	Value	Language
dc.contributor.author	Satapathy, J K	-
dc.contributor.author	Subhashini, K R	-
dc.date.accessioned	2009-05-30T01:22:15Z	-
dc.date.available	2009-05-30T01:22:15Z	-
dc.date.issued	2008	-
dc.identifier.citation	Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications, Pages 79-85	en
dc.identifier.uri	http://hdl.handle.net/2080/877	-
dc.description.abstract	This paper presents a new approach to equalization of communication channels using Artificial Neural Networks (ANNs). A novel method of training the ANNs using Tabu based Back Propagation (TBBP) Algorithm is described. The algorithm uses the Tabu Search (TS) to improve the performance of the equalizer as it searches for global minima which is many a time escaped while Back Propagation (BP) algorithm is applied for this purpose. From the results it can be noted that the proposed algorithm improves the classification capability of the ANNs in differentiating the received data.	en
dc.format.extent	250179 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.subject	Artificial Neural Networks	en
dc.subject	Tabu Search	en
dc.subject	Local Minima	en
dc.subject	Global Solution	en
dc.subject	Decision Feed Back	en
dc.subject	Aspiration Criterion	en
dc.title	A Tabu Based Neural Network Training Algorithm for Equalization of Communication Channels	en
dc.type	Article	en
Appears in Collections:	Conference Papers

Files in This Item:

File	Description	Size	Format
subhashini1.pdf		244.32 kB	Adobe PDF	View/Open