| 一类基于统计理论的神经网络模式识别方法 |
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| 引用本文： | 罗中良,麦宜佳,施仁. 一类基于统计理论的神经网络模式识别方法[J]. 工业仪表与自动化装置, 2001, 0(6): 5-7 |
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| 作者姓名： | 罗中良 麦宜佳 施仁 |
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| 作者单位： | 1. 佛山科学技术学院自动化系,
2. 西安交通大学自动控制系, |
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| 摘 要： | 本文针对用人工神经网络进行模式识别时样本特征指标过多的问题,提出用统计理论的主成分分析方法对数据进行预处理,再选出几个主成分作为神经网络的输入节点,从而极大地简化人工神经网络,提高了模式识别的效果。
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| 关 键 词： | 主成分分析 神经网络 模式识别 统计理论 |
| 文章编号： | 1000-0682(2001)06-0005-03 |
| 修稿时间： | 2000-03-12 |
A model identification method involved with a neural network based on a statistic theory |
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| LUO Zhong liang ,MAI Yi jia ,SHI Ren. A model identification method involved with a neural network based on a statistic theory[J]. Industrial Instrumentation & Automation, 2001, 0(6): 5-7 |
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| Authors: | LUO Zhong liang MAI Yi jia SHI Ren |
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| Affiliation: | LUO Zhong liang 1,MAI Yi jia 1,SHI Ren 2 |
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| Abstract: | In general a neural network is used to identify models,in which the number of sample's characteristic variables is too large to deal with.An improved method has been developed by analysing principal components to pretreat datasets.Some principal components are selected as the input nodes of the neural network.Based on the above,the network is simplified and the result of recognition has got improved. |
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| Keywords: | Principal component analysis Neural network Model identification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
