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A Probabilistic Neural Network for Designing Good Codes

Babu, Phanendra G and Murty, Narasimha M (1993) A Probabilistic Neural Network for Designing Good Codes. In: 1993 International Joint Conference on Neural Networks (IJCNN '93-Nagoya), 25-29 October, Japan, Vol.2, 1590 -1593.

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Abstract

Designing good error-correcting codes typically requires searching in search spaces. The vastness of search space precludes the use of brute force techniques such as exhaustive enumeration. The problem of designing codes so that each code repels others(in the sense of hamming distance) fits well in the framework of neural networks. Formulating an energy function to design codes is very difficult and cannot satisfactorily be solved by Hopfield neural network model. To alleviate these problems, a probabilistic neural network model is proposed.The usefulness of the proposed model is investigated with respect to maximal distance codes and constant weight codes. Results of some code parameters that have been designed using the proposed model are presented.

Item Type: Conference Paper
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Additional Information: Copyright 1990 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.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 22 Aug 2008
Last Modified: 18 Jun 2014 10:03
URI: http://eprints.iisc.ernet.in/id/eprint/6800

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