一个双循环回声状态网络拓,用于时间序列预测
Jun Fu1, Guangli Li1, Jianfeng Tang1
1College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China.
Chaos (Woodbury, N.Y.)
|September 11, 2023
概括
一个新的双循环回声状态网络 (DCESN) 通过使用固定权重和更简单的连接来改善时间序列预测. 与传统的回声国家网络 (ESN) 相比,这提高了性能,稳定性和硬件实现.
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
- 时间序列分析时间序列分析
背景情况:
- 响应状态网络 (ESN) 是有效的时间序列预测由于他们的储库计算属性.
- 传统的ESN由于随机和复杂的水库连接而遭受性能不稳定和高计算成本.
- 由于ESN固有的随机性和复杂性,ESN的硬件实现具有挑战性.
研究的目的:
- 提出一个双循环回声国家网络 (DCESN),以解决传统ESN的局限性.
- 为了提高预测性能,稳定性,并减少计算时间.
- 为更广泛的ESN应用程序简化硬件实现.
主要方法:
- 根据Li-ESN模型开发了一个DCESN.
- 实施固定权重,以提高可预测性和稳定性.
- 简化了水库连接,以减少计算复杂性.
主要成果:
- 在各种数据集中,DCESN表现出与传统ESN相比的可比或优越的预测性能.
- 该模型显示了对噪声和参数波动的稳定性.
- 观察到减少计算时间和简化网络结构.
结论:
- DCESN为时间序列预测提供了更稳定,更有效的替代方案.
- 固定的重量和更简单的连接使硬件实现更容易.
- 在时间序列建模中,DCESN显示了未来应用的巨大潜力.
相关概念视频
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