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一种具有自激吸引子的新型混沌系统：熵测量、信号加密与参数估计

A New Chaotic System with a Self-Excited Attractor: Entropy Measurement, Signal Encryption, and Parameter Estimation.

作者信息

Xu Guanghui, Shekofteh Yasser, Akgül Akif, Li Chunbiao, Panahi Shirin

机构信息

School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China.

Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China.

出版信息

Entropy (Basel). 2018 Jan 27;20(2):86. doi: 10.3390/e20020086.

DOI:10.3390/e20020086
PMID:33265177
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7512649/
Abstract

In this paper, we introduce a new chaotic system that is used for an engineering application of the signal encryption. It has some interesting features, and its successful implementation and manufacturing were performed via a real circuit as a random number generator. In addition, we provide a parameter estimation method to extract chaotic model parameters from the real data of the chaotic circuit. The parameter estimation method is based on the attractor distribution modeling in the state space, which is compatible with the chaotic system characteristics. Here, a Gaussian mixture model (GMM) is used as a main part of cost function computations in the parameter estimation method. To optimize the cost function, we also apply two recent efficient optimization methods: WOA (Whale Optimization Algorithm), and MVO (Multi-Verse Optimizer) algorithms. The results show the success of the parameter estimation procedure.

摘要

在本文中，我们介绍了一种用于信号加密工程应用的新型混沌系统。它具有一些有趣的特性，并且通过实际电路作为随机数发生器成功实现并制造出来。此外，我们提供了一种参数估计方法，用于从混沌电路的实际数据中提取混沌模型参数。该参数估计方法基于状态空间中的吸引子分布建模，这与混沌系统特性相兼容。这里，高斯混合模型（GMM）用作参数估计方法中成本函数计算的主要部分。为了优化成本函数，我们还应用了两种最新的高效优化方法：鲸鱼优化算法（WOA）和多宇宙优化器（MVO）算法。结果表明了参数估计过程的成功。

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