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。
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.
出处
《工业仪表与自动化装置》
2001年第6期5-7,共3页
Industrial Instrumentation & Automation
关键词
主成分分析
神经网络
模式识别
统计理论
Principal component analysis
Neural network
Model identification
