Constant-time neural decoders for some BCH codes

Yuen Hsien Tseng, Ja Ling Wu

研究成果: 書貢獻/報告類型會議論文篇章

3 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994
頁面343
頁數1
DOIs
出版狀態已發佈 - 1994
對外發佈
事件1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, 挪威
持續時間: 1994 6月 271994 7月 1

出版系列

名字IEEE International Symposium on Information Theory - Proceedings
ISSN(列印)2157-8095

其他

其他1994 IEEE International Symposium on Information Theory, ISIT 1994
國家/地區挪威
城市Trondheim
期間1994/06/271994/07/01

ASJC Scopus subject areas

  • 理論電腦科學
  • 資訊系統
  • 建模與模擬
  • 應用數學

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