### Abstract

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 language | English |
---|---|

Title of host publication | Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994 |

Publisher | IEEE |

Number of pages | 1 |

ISBN (Print) | 0780320158, 9780780320154 |

DOIs | |

Publication status | Published - 1994 Dec 1 |

Event | 1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, Norway Duration: 1994 Jun 27 → 1994 Jul 1 |

### Publication series

Name | IEEE International Symposium on Information Theory - Proceedings |
---|---|

ISSN (Print) | 2157-8095 |

### Other

Other | 1994 IEEE International Symposium on Information Theory, ISIT 1994 |
---|---|

Country | Norway |

City | Trondheim |

Period | 94/6/27 → 94/7/1 |

### Fingerprint

### ASJC Scopus subject areas

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

### Cite this

*Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994*[394675] (IEEE International Symposium on Information Theory - Proceedings). IEEE. https://doi.org/10.1109/ISIT.1994.394675