使用回声状态网络预测衰老过渡
Biswambhar Rakshit1, Aryalakshmi S1, Arjun J Kartha1
1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India.
Chaos (Woodbury, N.Y.)
|August 3, 2023
概括
神经网络可以预测合振荡器何时停止工作. 一个简单的回声状态网络 (ESN) 可以准确地预测系统崩,并识别复杂网络中的关键过渡.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 计算神经科学是一种神经科学.
背景情况:
- 结合的非线性振荡器可以在不活性元素的关键部分停止活动,这种现象被称为衰老过渡.
- 对这种过渡的分析预测通常依赖于集群同步观点.
- 像神经网络这样的无模型,数据驱动的方法来预测这种过渡的潜力仍然是一个开放的问题.
研究的目的:
- 调查神经网络框架,特别是回声状态网络 (ESN) 在预测合非线性振荡器中衰老过渡的能力.
- 确定ESN是否能够准确预测时间演变和系统崩的开始.
- 评估ESN识别关键参数的能力,并在不同的网络拓中进行概括.
主要方法:
- 使用一个简单的回声状态网络 (ESN) 与训练出来的权重.
- 训练ESN使用来自合范式极限周期振荡器的数据.
- 测试了ESN在所有对所有,小世界和无尺度网络拓学的预测能力,使用来自崩前制度或平均场动态的最小训练数据 (两个节点).
主要成果:
- ESN准确地预测了合振荡器系统的时间演变和崩的开始.
- 在各种网络结构中,ESN成功地确定了不活跃振荡器的关键部分.
- 在ESN展示了预测老龄化过渡的能力,即使训练有有限的数据或平均场动态.
结论:
- 一种无模型,数据驱动的ESN方法可以有效地预测合非线性振荡器系统的衰老过渡.
- 在没有先前的系统知识的情况下,ESN为预测系统崩和识别关键动态提供了一个强大的工具.
- 这个框架对分析和预测复杂的动态网络中新出现的现象充满希望.
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