https://scholars.lib.ntu.edu.tw/handle/123456789/307807
標題: | Constant-time neural decoders for some BCH codes | 作者: | Tseng, Yuen-Hsien JA-LING WU |
公開日期: | 1994 | 來源出版物: | IEEE International Symposium on Information Theory | 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. © 1994 IEEE. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84894379706&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/307807 |
DOI: | 10.1109/ISIT.1994.394675 |
顯示於: | 資訊工程學系 |
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