Measuring the Complexity of Chaotic Time Series by Fuzzy Entropy

A bridge called chaotic time series connects the chaos theory and real world. The properties of chaos make a natural relationship with security problem about information, such as sensitive dependence on initial conditions, unpredictable result for long and so on. With the rapid development of communication and the Internet technologies in information time, the security problem about information has become a hot focus in our daily life. Accordingly the implementation of chaotic theory is becoming widely used, which makes the complexity analysis of the chaotic time series become the highlights. The meaning of complexity is hard to make sense. When applied in high-security multiple-access communication systems, the high complexity makes the sequence obscure and hard to analysis. In this paper, an introduction of a new complexity measurement to evaluate the chaotic time series based on the Fuzzy Entropy is given. The results of simulations and analysis illustrates that, it is the FuzzyEn scheme that makes it possible to have a better understanding of the changing complexities of the sequences.

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