多层感知子网络优化用于混乱时间序列建模
Mu Qiao1,2, Yanchun Liang3,4, Adriano Tavares2
1School of Mathematics, Jilin University, Changchun 130021, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种优化的多层感知子 (MLP) 方法,用于混乱时间序列分析. 该方法通过使用概括的自由度近似和阿卡奇信息标准来进行模型选择来提高多步预测的准确性.
科学领域:
- 复杂系统科学 复杂系统科学
- 计算神经科学是一种神经科学.
- 数据科学数据科学数据科学
背景情况:
- 混乱的时间序列表现出固有的随机性和非线性,对准确的中期和长期预测提出了重大挑战.
- 多层感知器 (MLP) 网络提供了一个强大的框架,用于模拟混乱系统中发现的复杂,非线性动态.
研究的目的:
- 使用多层感知器 (MLP) 网络开发一个用于混乱时间序列分析的优化框架.
- 通过一种新的近似方法和信息标准,提高混乱时间序列预测的精度.
主要方法:
- 为MLP网络开发了一个通用的自由度近似方法.
- 阿卡奇信息标准是作为模型培训的损失函数推导和实现的.
- 该框架整合了相位空间重建,模型训练和对混乱时间序列的模型选择.
主要成果:
- 提议的优化MLP方法在从候选模型中选择最佳模型方面表现出有效性.
- 对人工和现实世界的混乱时间序列的数值应用验证了该方法的性能.
- 优化的模型在多步预测任务中实现了高精度.
结论:
- 开发的框架为混乱时间序列建模和预测提供了有效的方法.
- 一般化的自由度近似和阿卡奇信息标准提高了MLP处理混乱动态的能力.
- 这项研究在准确预测复杂,不可预测的时间序列数据方面取得了重大进展.
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