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在线动态合奏深度随机向量功能链接神经网络用于预测

Ruobin Gao¹, Ruilin Li², Minghui Hu²

¹School of Civil & Environmental Engineering, Nanyang Technological University, Singapore.

Neural networks : the official journal of the International Neural Network Society

|July 22, 2023

概括

本研究引入了一个动态集合深随机向量功能链接 (edRVFL) 模型,用于在线时间序列分析. 新的三阶段方法增强了时间特征提取,并适应变化的数据分布.

科学领域:

人工智能的人工智能
机器学习机器学习
数据科学数据科学数据科学

背景情况:

传统的集体深随机向量功能链接 (edRVFL) 模型并没有针对在线学习进行优化.
在edRVFL特征中固有的随机性可能会阻碍提取有意义的时间模式.
现有的方法缺乏强大的机制来适应不断变化的时间序列数据分布.

研究的目的:

为时间序列分析提出一个新的三阶段在线深度学习模型.
扩展edRVFL模型的动态和适应性学习能力.
改进时间特征的提取,处理时间序列中的数据分布转移.

主要方法:

开发一个动态的edRVFL模型,包括在线分解,在线培训和在线动态合奏组件.
使用在线分解技术,为特征工程量身定制edRVFL架构.
设计一个在线学习算法,以提供高效的edRVFL模型培训,以及一个在线动态组合,用于输出聚合.

主要成果:

拟议的动态edRVFL模型在在线时间序列分析中表现出有效的性能.
三阶段的在线方法成功地解决了原始edRVFL模型的局限性.
对16个时间序列数据集的比较评估显示了与最先进的方法相比具有竞争力的结果.

结论:

动态的edRVFL模型为在线时间序列学习提供了强大的和适应性的解决方案.
在线分解,培训和组合方法的整合增强了时间特征的表示.
这项研究在将深度学习模型应用于动态时间序列数据方面取得了重大进展.

关键词:

持续的学习持续的学习深度学习是一种深度学习.预测预测预测预测机器学习是机器学习.在线学习在线学习.随机向量功能链路网络的随机向量.

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