Improved Learning Algorithm for Self-adaptive Neural Nets Based on Principal Component Analysis

被引：0
|
作者：
Du, Shujie [1 ]
机构：
[1] Ocean Univ China, Ctr Comp, Qingdao 266100, Shandong, Peoples R China
来源：
INFORMATION COMPUTING AND APPLICATIONS, PT 1 | 2012年 / 307卷
关键词：
PCA; dimensionality reduction; self-adaptive feature extraction; eigenvector; convergence;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an analysis of the reason considering principal component analysis as a quantitatively rigorous method to remove redundant information from source cube, and gives the mathematical equation derivation for solving single and multiple principal components in multiple input and multiple output nets, puts forward the learning algorithm deduction of self-adaptive principal component feature extraction in neural network. The validity of the presented algorithm is demonstrated with simulated data at last.
引用
收藏
页码：16 / 24
页数：9
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