Abstract
To further explain the computational principles of the new features of the complexity metrics we reported, we first explain the heuristic information and new variable definition methods proposed by our algorithms, as well as the construction creep (CC) rate defined by the self-similar characterization in the new 3D variable space. Two typical nonlinear equations are applied as test cases. Numerical computing results show that the geometric expression of 3D features is more realistic than 0-1 test for chaos, and the key threshold parameter of the similarity measure in solid value can discover higher complexity than Kaplan–Yorke dimension. This case study on signal complexity measure will push new applications for the healthy growing of chaos criteria tree.
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Acknowledgements
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 Projects of Laboratory of Modern Acoustics of MOE [No. 2017-001].
Thank Mr. Peng Ming for running our new encryption algorithm.
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Cai, J., Chen, X., Ming, P., Li, W. (2021). Signal Complexity Measure Based on Construction Creep Rate in 3s Feature Space. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_2
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DOI: https://doi.org/10.1007/978-981-15-5887-0_2
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