Two Entropy-Based Criteria Design for Signal Complexity Measures

CAI Jinwei; LI Yaotian; LI Wenshi; LI Lei

doi:10.1049/cje.2019.07.008

Volume 28 Issue 6

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CAI Jinwei, LI Yaotian, LI Wenshi, et al., “Two Entropy-Based Criteria Design for Signal Complexity Measures,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1139-1143, 2019, doi: 10.1049/cje.2019.07.008

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CAI Jinwei, LI Yaotian, LI Wenshi, et al., “Two Entropy-Based Criteria Design for Signal Complexity Measures,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1139-1143, 2019, doi: 10.1049/cje.2019.07.008

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Two Entropy-Based Criteria Design for Signal Complexity Measures

doi: 10.1049/cje.2019.07.008

1.
Department of Microelectronics, Soochow University, Suzhou 215006, China;
2.
Department of Chemical and Materials Engineering, University of Alberta, Edmonton AB T6G 2V4, Canada

Funds: This work is supported by Technological Innovation of Key Industries in Suzhou City Prospective Application Study (No.SYG201701), Graduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX18_2509) and the Open Project of Laboratory of Modern Acoustics of MOE (No.2017_001).

More Information

Corresponding author: LI Wenshi (corresponding author) was born in Harbin,Heilongjiang Province,China,in 1963.He received the B.S.degree in 1987 in physics from Harbin Normal Univ.,and received M.S.degree in electronics at Nanjing Univ.in 1990.In 2005,he did his microelectronics Ph.D.at Southeast Univ.Prof.LI has worked in Department of Microelectronics at Soochow University since 2001,he is working at the subnano scale with new synaptic circuit to try and understand no less than how the braincircuitry really works.(Email:lwshi@suda.edu.cn)
Received Date: 2018-07-21
Rev Recd Date: 2018-11-25
Publish Date: 2019-11-10

Abstract

Abstract

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 measure,
- Compression entropies (3s-graph),
- Construction creep (CC) rate

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References(22)

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