用卷积生成对抗网络评价混沌时间序列的生成

IF 0.4 Q4 MATHEMATICS, APPLIED JSIAM Letters Pub Date : 2023-01-01 DOI:10.14495/jsiaml.15.117
Yuki Tanaka, Yutaka Yamaguti
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引用次数: 0

摘要

为了了解卷积神经网络生成模拟复杂时间信号的时间序列的能力和局限性,我们训练了一个由卷积网络组成的生成对抗网络来生成混沌时间序列,并使用非线性时间序列分析来评估生成的时间序列。确定性和李雅普诺夫指数的数值测量表明,生成的时间序列很好地再现了原始时间序列的混沌特性。然而,误差分布分析表明,大误差出现在低但不可忽略的率。如果假设分布呈指数分布,就不会出现这样的误差。
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Evaluating generation of chaotic time series by convolutional generative adversarial networks
To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. A numerical measure of determinism and the Lyapunov exponent showed that the generated time series well reproduce the chaotic properties of the original time series. However, error distribution analyses showed that large errors appeared at a low but non-negligible rate. Such errors would not be expected if the distribution were assumed to be exponential.
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来源期刊
JSIAM Letters
JSIAM Letters MATHEMATICS, APPLIED-
自引率
25.00%
发文量
27
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