IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM

Wei Xu; Qun Ding; Xiaogang Zhang

doi:10.11948/2015024

2015 Volume 5 Issue 2

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Wei Xu, Qun Ding, Xiaogang Zhang. IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM[J]. Journal of Applied Analysis & Computation, 2015, 5(2): 262-272. doi: 10.11948/2015024

Citation:

Wei Xu, Qun Ding, Xiaogang Zhang. IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM[J]. Journal of Applied Analysis & Computation, 2015, 5(2): 262-272. doi: 10.11948/2015024

IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM

1 School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China;
2 School of Electronic Engineering, Heilongjiang University, Harbin 150080, China;
3 Education Office of Heilongjiang Province, Harbin 150001, China

Fund Project:

Abstract

Abstract

The principal component analysis (PCA) is an effective statistical analysis method in statistical data analysis, feature extraction and data compression. The method simplifies multiple related variables into a linear combination of several irrelevant variables, through the less-comprehensive index as far as possible to replace many of the original data, and can reflect the information provided by the original data. This paper studies the signal feature extraction algorithm based on PCA, and extracts sequences' feature which generated by Logistic mapping. Then we measured the complexity of the reconstructed chaotic sequences by the permutation entropy algorithm. The testing results show that the complexity of the reconstruction sequences is significantly higher than the original sequences.
- Chaos /
- complexity /
- permutation entropy /
- principal component analysis
MSC: 37M25

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