A new series-wound framework for generating 1D chaotic maps
Zhongyun Hua, Yicong Zhou, C. L. Philip Chen
Abstract
Zhongyun Hua, Yicong Zhou, C. L. Philip Chen
Abstract
This paper introduces a series-wound framework to generate a large number of new one-dimensional (1D) chaotic maps using a combination of two different 1D chaotic maps (called seed maps). Examples and experimental analysis demonstrate that the newly generated chaotic maps have more parameters, larger chaotic ranges, and better chaotic behaviors than their corresponding seed maps.
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This paper introduces a series-wound framework to generate a large number of new one-dimensional (1D) chaotic maps using a combination of two different 1D chaotic maps (called seed maps). Examples and experimental analysis demonstrate that the newly generated chaotic maps have more parameters, larger chaotic ranges, and better chaotic behaviors than their corresponding seed maps.
Key concepts: Chaotic, Series (stratigraphy), Chaotic map, Computer science, Chaotic systems, Time series, Algorithm, Statistical physics