通过多模态深度神经网络识别噪声时间序列中的混沌动力学

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa
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引用次数: 0

摘要

混沌检测是一个识别一系列测量值是否从一组潜在的混沌动力学中采样的问题。测量噪声的不可避免的存在极大地影响了混沌检测器的性能,因为从随机信号中辨别混沌动力学变得更具挑战性。本文介绍了一种计算效率高的多模态深度神经网络,该网络结合了时间序列分析、递推图和频谱图的信息,专为混沌检测量身定制。所提出的方法是第一种适用于混沌系统多类分类的方法,同时对测量噪声具有鲁棒性,并在受白色、粉色或棕色噪声影响的 15 种不同混沌和非混沌动力学数据集上进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying chaotic dynamics in noisy time series through multimodal deep neural networks
Chaos detection is the problem of identifying whether a series of measurements is being sampled from an underlying set of chaotic dynamics. The unavoidable presence of measurement noise significantly affects the performance of chaos detectors, as discerning chaotic dynamics from stochastic signals becomes more challenging. This paper presents a computationally efficient multimodal deep neural network tailored for chaos detection by combining information coming from the analysis of time series, recurrence plots and spectrograms. The proposed approach is the first one suitable for multi-class classification of chaotic systems while being robust with respect to measurement noise, and is validated on a dataset of 15 different chaotic and non-chaotic dynamics subject to white, pink or brown colored noise.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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