## 抄録

This paper proposes the application of neural networks with multiplication units to parity-N problem, mirror symmetry problem and a function approximation problem. It is clear that, higher-order terms in neural networks, such as sigma-pi unit, can improve the computational power of neural networks considerably. But how the real neurons do this is still unclear. We have used one multiplication unit to construct full higher-order terms of all the inputs, which was proved very efficient for parity-N problem. Our earlier work on applying multiplication units to other problems suffered from the drawback of gradient-based algorithm, such as backpropagation algorithms, for being easy to stuck at local minima due to the complexity of the network. In order to overcome this problem we consider a novel random search, RasID, for the training of neural networks with multiplication units, which does an intensified search where it is easy to find good solutions locally and a diversified search to escape from local minima under a pure random search scheme. The method shows its advantage on the training of neural networks with multiplication units.

本文言語 | English |
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ホスト出版物のタイトル | ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age |

出版社 | Institute of Electrical and Electronics Engineers Inc. |

ページ | 75-79 |

ページ数 | 5 |

巻 | 1 |

ISBN（印刷版） | 9810475241, 9789810475246 |

DOI | |

出版ステータス | Published - 2002 |

外部発表 | はい |

イベント | 9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore 継続期間: 2002 11月 18 → 2002 11月 22 |

### Other

Other | 9th International Conference on Neural Information Processing, ICONIP 2002 |
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国/地域 | Singapore |

City | Singapore |

Period | 02/11/18 → 02/11/22 |

## ASJC Scopus subject areas

- コンピュータ ネットワークおよび通信
- 情報システム
- 信号処理