Open AccessTwo Entropy‐Based Criteria Design for Signal Complexity Measures
Author(s) -
CAI Jinwei,
LI Yaotian,
Li Wenshi,
Li Lei
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.07.008
Subject(s) - chaotic , lyapunov exponent , entropy (arrow of time) , mathematics , sample entropy , computer science , algorithm , nonlinear system , lorenz system , theoretical computer science , artificial intelligence , pattern recognition (psychology) , physics , quantum mechanics
Signal complexity denotes the intricate patterns hidden in the complicated dynamics merging from nonlinear system concerned. The chaotic signal complexity measuring in principle combines both the information entropy of the data under test and the geometry feature embedded. Starting from the information source of Shannon's entropy, combined with understanding the merits and demerits of 0‐1 test for chaos, we propose new compression entropy criteria for identifying chaotic signal complexity in periodic, quasi‐periodic or chaotic state, in mapping results in 3 s ‐graph with significant different shape of good or bad spring and in Construction creep (CC) rate with distinguishable value‐range of [0, 7%], (7%, 50%] or (50%, 84%]. The employed simulation cases are Lorenz, Li and He equations' evolutions, under key information extracting rules of both two‐layer compression functions and self‐similarity calcu‐lation, compared with methods of 0‐1 test for chaos, Lyapunov exponent and Spectral Entropy complexity. The research value of this work will provide deep thinking of the concise featureexpressions of chaotic signal complexity measure in feature domain.