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Chaos Criteria Design Based on Modified Sign Functions with One or Three-Threshold

Chaos Criteria Design Based on Modified Sign Functions with One or Three-Threshold

作     者:WU Senlin LI Yaotian LI Wenshi LI Lei WU Senlin;LI Yaotian;LI Wenshi;LI Lei

作者机构:Department of Microelectronics Soochow University Laboratory of Modern Acoustics of MOE Nanjing University Department of Chemical and Materials Engineering University of Alberta 

出 版 物:《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期:2019年第28卷第2期

页      面:364-369页

核心收录:

学科分类:0808[工学-电气工程] 07[理学] 0809[工学-电子科学与技术(可授工学、理学学位)] 070201[理学-理论物理] 0701[理学-数学] 0702[理学-物理学] 

基  金:supported by the Natural Science Foundation of Jiangsu Province of China(No.BK20141196) Technological Innovation of Key Industries in Suzhou City Prospective Application Study(No.SYG201701) RIGOL University-Enterprise Cooperative Project of Ministry of Education(No.201702125008) 

主  题:Chaos criteria Symbolic dynamics Data compression Simplest 0-1 test Signed compression plot Ks metric Poincare section Lyapunov index 

摘      要:The complexity measures of chaotic or periodic signals are perpetual topics of interest to data scientists. This work adheres to the framework of the traditional 0-1 test for chaos and replaces sine and cosine functions by modified sign functions. The compressive mapping rules chosen are one-threshold of three-value or three-threshold of five-value. In new criteria for chaos in forms of the 3s plot and Ks metric compared with 0-1 test results, the periodic state of data features a short beeline instead of a big ring in the pq plot and signs the nearest zero mark, while the chaotic state signs a simple curve instead of a random-walking shape in the pq plot, and shows the nearest one mark. By computing the Lorenz equation evolution under the contrast tests of the Poincare section and Lyapunov index, we visualize a new chaoscriteria design in symbolic dynamics and data compression principles, and our work may lay the foundation for further expressing the chaotic appearance of novel signals deep into future brainets.

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