用回声状态网络模拟混沌系统中的罕见事件
Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems
- 用回声状态网络学习竞争洛特卡-沃尔泰拉模型的混沌吸引子
- 成功复现变量分布尾部及罕见事件的概率特征
- 适合研究复杂系统中极端事件的建模与预测
我们将回声状态网络（Echo-State Networks）应用于混沌状态下的竞争洛特卡-沃尔泰拉模型的时间序列预测与统计特性分析。结果表明，该网络能够成功学习系统的混沌吸引子，并准确复现各依赖变量的分布直方图，包括尾部区域和罕见事件。在非平衡模拟条件下，网络仍能有效重现罕见事件的发生模式。通过广义极值分布（Generalized Extreme Value distribution）量化尾部行为，验证了模型对极端事件概率特征的捕捉能力。
原文摘要 · Abstract (English)
We apply Echo-State Networks to predict time series and statistical properties of the competitive Lotka-Volterra model in the chaotic regime. In particular, we demonstrate that Echo-State Networks successfully learn the chaotic attractor of the competitive Lotka-Volterra model and reproduce histograms of dependent variables, including tails and rare events. We also demonstrate that the Echo-State Networks reproduce rare events in the non-equilibrium simulations of the Lotka-Volterra system. We use the Generalized Extreme Value distribution to quantify the tail behavior.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。