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arXiv 2104.01743nlin.CD
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用环态收缩算法构造高维数字混沌系统

Constructing Higher-Dimensional Digital Chaotic Systems via Loop-State Contraction Algorithm

Qianxue Wang, Simin Yu, Christophe Guyeux, Wei Wang

摘要:

近年来,在有限精度数字域中产生严格可证混沌在理论和实践上都取得了很大的进展,本文就是其中的一部分,旨在完善和拓展高维数字混沌系统(HDDCS)的理论和应用框架。本文首先从理论上严格证明了HDDCS的拓扑混合性。拓扑混合蕴涵着紧空间中的Devaney混沌定义,但反之则不然。因此,拓扑混合的证明完善和扩展了HDDCS的理论框架。然后,给出了一种用回路状态收缩算法构造HDDCS的一般设计方法。本文的出发点是构造不受随机序列控制的迭代函数(以下简称迭代函数)。在此基础上,提出了解决HDDCS施工问题的一般设计方法,并通过实例说明了该方法的有效性和可行性。利用所设计的HDDCS对应的邻接矩阵构造混沌回声状态网络(ESN),并用于Mackey-Glass时间序列预测。预测精度随油藏规模和HDDCS维数的增加而提高,说明高维系统比低维系统具有更好的预测性能。

英文摘要:

In recent years, the generation of rigorously provable chaos in finite precision digital domain has made a lot of progress in theory and practice, this article is a part of it. It aims to improve and expand the theoretical and application framework of higher-dimensional digital chaotic system (HDDCS). In this study, topological mixing for HDDCS is strictly proved theoretically at first. Topological mixing implies Devaney's definition of chaos in compact space, but not vise versa. Therefore, the proof of topological mixing improves and expands the theoretical framework of HDDCS. Then, a general design method for constructing HDDCS via loop-state contraction algorithm is given. The construction of the iterative function uncontrolled by random sequences (hereafter called iterative function) is the starting point of this research. On this basis, this article put forward a general design method to solve the problem of HDDCS construction, and a number of examples illustrate the effectiveness and feasibility of this method. The adjacency matrix corresponding to the designed HDDCS is used to construct the chaotic echo state network (ESN) for the Mackey-Glass time series prediction. The prediction accuracy is improved with the increase of the size of the reservoir and the dimension of HDDCS, indicating that higher-dimensional systems have better prediction performance than lower-dimensional systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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