Dept. of Comput. Sci. & Inf. Eng., National Taiwan Univ.Tseng, Yuen-HsienYuen-HsienTsengJA-LING WU2018-09-102018-09-101994http://www.scopus.com/inward/record.url?eid=2-s2.0-84894379706&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/307807High-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.application/pdf82065 bytesapplication/pdfconference paper10.1109/ISIT.1994.3946752-s2.0-84894379706