| 高阶神经网络连接权的稀疏化及其删减算法 |
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| 引用本文： | 李守丽, 李金艳, 李望超. 高阶神经网络连接权的稀疏化及其删减算法[J]. 电子与信息学报, 1999, 21(2): 182-185. |
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| 作者姓名： | 李守丽 李金艳 李望超 |
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| 作者单位： | 河北工业大学计算机系，The UniVersity of Melbourne，Australia，河北工业大学计算机系 天津 300130，天津 300130 |
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| 摘 要： | 本文首先研究完全连接型高阶神经网络的逼近能力,并证明了定义在{0,1}N上的任意布尔函数都可以由完全连接的高阶神经网络来实现。接着提出了旨在简化网络结构的去除冗余连接权删减算法,并用于高阶神经分类器的稀疏化实现。模拟实验结果证明了这种算法的有效性。
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| 关 键 词： | 高阶神经网络 冗余连接权 稀疏化连接 删减算法 |
| 收稿时间： | 1997-08-25 |
| 修稿时间： | 1998-07-26 |
SPARSED CONNECTION WEIGHTS OF HIGHER-ORDER NEURAL NETWORK AND ITS PRUNING ALGORITHM |
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| Li Shouli, Li Jinyan, Li Wangchao. SPARSED CONNECTION WEIGHTS OF HIGHER-ORDER NEURAL NETWORK AND ITS PRUNING ALGORITHM[J]. Journal of Electronics & Information Technology, 1999, 21(2): 182-185. |
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| Authors: | Li Shouli Li Jinyan Li Wangchao |
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| Abstract: | In this paper, the fully-connected higher-order neuron and sparsed higher-order neuron are introduced, the mapping capabilities of the fully-connected higher-order neural networks are investigated, and that arbitrary Boolean function defined from {0,1}N can be realized by fully-connected higher-order neural networks is proved. Based on this, in order to simplify the networks' architecture, a pruning algorithm for eliminating the redundant connection weights is also proposed, which can be applied to the implementation of sparsed higher-order neural classifier. The simulated results show the effectiveness of the algorithm. |
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| Keywords: | Higher-order neural networks Redundant connection weights Sparsed connection Pruning algorithm |
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