Constant-time neural decoders for some BCH codes

Yuen Hsien Tseng, Ja Ling Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


High-order neural networks (HONN) are shown to decode some BCH codes in constant-time with very low hardware complexity. HONN is a direct extension of the linear perceptron: it uses a polynomial consisting of a set of product terms as its discriminant function. Because a product term is isomorphic to a parity function and a two-layer perceptron for the parity function has been shown by Rumelhart, Hinton, and Williams (1986), HONN has a simple realization if it is considered as having a set of parity networks in the first-half layer, followed by a linear perceptron in the second-half layer. The main problem in using high-order neural networks for a specific application is to decide a proper set of product terms. We apply genetic algorithms to this structure-adaptation problem.

Original languageEnglish
Title of host publicationProceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Print)0780320158, 9780780320154
Publication statusPublished - 1994
Externally publishedYes
Event1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, Norway
Duration: 1994 Jun 271994 Jul 1

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Other1994 IEEE International Symposium on Information Theory, ISIT 1994

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics


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