对回声状态网络参数的输入驱动优化,用于对混乱时间序列的预测
Leila Gonbadi1,2, Habib Rostami3,4, Ebrahim Sahafizadeh1
1Department of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 7516913817, Iran.
Scientific reports
|September 27, 2025
概括
优化回声状态网络 (ESN) 用于时间序列预测需要调整水库重量以适应输入数据. 这项研究引入了通过考虑数据特征和网络拓学的新方法来提高ESN性能.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 复杂的系统复杂的系统.
背景情况:
- 响应状态网络 (ESN) 对于时间序列预测是有效的,但依赖于随机的水库权重.
- 传统的ESN设计忽略了输入数据的特征,限制了预测的准确性.
- 水库结构,包括拓和重量,对ESN性能产生重大影响.
研究的目的:
- 为输入依赖的回声州网络水库设计开发一个理论框架.
- 建议和评估ESN储库的新型监督和半监督优化方法.
- 通过数据驱动的储库适应来证明ESN中预测准确度的提高.
主要方法:
- 开发了一个理论框架,将输入数据属性与最佳水库重量联系起来.
- 实施了一种监督方法,使用梯度下降来优化水库重量.
- 提出了一种半监督技术,将网络属性 (小世界,无规模) 与超参数调整相结合.
- 在合成 (Mackey-Glass,NARMA) 和现实世界的气候数据集上进行了实验.
主要成果:
- 拟议的方法在各种数据集中显著优于传统的随机权重ESN.
- 与传统的ESN方法相比,实现了较低的预测错误.
- 确定了边缘连接参数对网络性能具有高度影响,仅次于储大小.
- 证明了依赖输入的水库设计对于增强时间序列预测的重要性.
结论:
- 在ESN中,应根据输入数据特征调整水库重量,以实现最佳性能.
- 网络拓和权重都是影响预测准确性的关键因素.
- 提出的优化方法为设计更有效的ESN提供了实际指导方针.
- 这些发现为自动化,数据驱动的ESN水库优化铺平了道路.
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