IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM | Zendy

Wei Xu | Zendy; Qun Ding | Zendy; Xiaogang Zhang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM

Author(s) -

Wei Xu,

Qun Ding,

Xiaogang Zhang

Publication year - 2015

Publication title -

journal of applied analysis and computation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.55

H-Index - 21

eISSN - 2158-5644

pISSN - 2156-907X

DOI - 10.11948/2015024

Subject(s) - principal component analysis , pattern recognition (psychology) , chaotic , algorithm , entropy (arrow of time) , permutation (music) , feature extraction , computer science , data compression , feature (linguistics) , mathematics , artificial intelligence , data mining , linguistics , philosophy , physics , quantum mechanics , acoustics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research