RESEARCH INITIATION AWARD: New Methods in Neural Network Prediction: Using Fossil Records to Improve Solar Activity Forecasting

研究启动奖：神经网络预测新方法：利用化石记录改进太阳活动预测

• 批准号: 9410823
• 负责人: Eric Wan
• 金额: $ 10万
• 依托单位: Oregon Graduate Institute of Science & Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-15 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9410823&HistoricalAwards=false
• 关键词: RESEARCH; INITIATION; AWARD; New; Methods

9410823 Wan In recent years, neural networks have become increasingly popular for use in time series prediction. The principle investigator has developed specialized neural network architectures that have shown improvements over traditional neural predictors. this grant will allow continued development of these architectures while studying the extremely important times series discussed below. One of the most famous time series corresponds to the record of sunspots dating back to the 1700's. New insight in to this series come s form fossilized rocks formed during the Precambrian period in South Australia. Remarkably, striation widths in the fossils are believed to constitute over a thousand year record of solar activity. The shape and spectral characteristics are near identical to the support series. though obviously noisy, the data is rich in information. Using neural networks, the PI will research how this series may be used to improve our current predictions of sunspots. In general, it is an important problem of how related series at different times may be used to improve the prediction of one of them. From a practical perspective, the use for sunspot predictions are found in applications ranging form planning orbital space missions to safeguarding power grids.

9410823 Wan近年来，神经网络在时间序列预测中的应用越来越普遍。 主要研究者开发了专门的神经网络架构，这些架构比传统的神经预测器有所改进。 这笔赠款将使这些体系结构的继续发展，同时研究下面讨论的极其重要的时间序列。 最著名的时间序列之一对应于太阳黑子的记录，可以追溯到18世纪。 对这一系列的新认识来自于形成于南澳前寒武纪时期的石化岩石。 值得注意的是，化石中的条纹宽度被认为构成了一千多年的太阳活动记录。 形状和光谱特性与支持系列几乎相同。 虽然这些数据明显带有噪声，但信息丰富。 使用神经网络，PI将研究如何使用这一系列来改善我们目前对太阳黑子的预测。 一般来说，如何利用不同时间的相关序列来改进其中一个序列的预测是一个重要的问题。 从实际的角度来看，太阳黑子预测的应用范围从规划轨道空间任务到保护电网。