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Two Entropy-Based Criteria Design for Signal Complexity Measures

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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 3s-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.

Chaotic signal complexity measureCompression entropies (3s-graph)Construction creep (CC) rate

CAI Jinwei、LI Yaotian、LI Wenshi、LI Lei

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Department of Microelectronics, Soochow University, Suzhou 215006, China

Department of Chemical and Materials Engineering, University of Alberta, Edmonton AB T6G 2V4, Canada

This work is supported by Technological Innovation of Key Industries in Suzhou City Prospective Application StudyGraduate Research & Practice Innovation Program of Jiangsu ProvinceOpen Project of Laboratory of Modern Acoustics of MOE

SYG201701KYCX18_25092017_001

2019

10.1049/cje.2019.07.008

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(6)
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