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

Title:	A Tabu Based Neural Network Training Algorithm for Equalization of Communication Channels
Authors:	Satapathy, J K Subhashini, K R
Keywords:	Artificial Neural Networks Tabu Search Local Minima Global Solution Decision Feed Back Aspiration Criterion
Issue Date:	2008
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
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.
URI:	http://hdl.handle.net/2080/877
Appears in Collections:	Conference Papers

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