Open AccessNew complexity metric of chaotic pseudorandom sequences using fuzzy relationship entropy
Author(s) -
Xiaojun Chen,
Zan Li,
Baoming Bai,
Jueping Cai
Publication year - 2011
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.60.064215
Subject(s) - pseudorandom number generator , pseudorandomness , randomness , chaotic , pseudorandom generator theorem , symbolic dynamics , entropy (arrow of time) , algorithm , computer science , mathematics , metric (unit) , statistical physics , theoretical computer science , artificial intelligence , physics , pure mathematics , statistics , operations management , economics , quantum mechanics
A new complexity metric to evaluate the unpredictability of the chaotic pseudorandom sequences based on the fuzzy relationship entropy (F-REn) is proposed in the view of maximal randomness of the sequences with arbitrary length. On this condition,two basic properties of F-REn are proved. Simulations and analysis results show that, the proposed F-REn works effectively to discern the changing complexities of the chaotic pseudorandom sequences, and compared with complexity metric based on the approximate entropy(ApEn) and symbolic dynamics approach , F-REn works have obvious advantages in the applicability of symbolic space, the sensitivity of vector dimension and the robustness of resolution parameter.