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

9410823近年来，神经网络在时间序列预测中的应用越来越普遍。这位原理研究人员开发了专门的神经网络结构，显示出比传统神经预报器的改进。这笔赠款将允许继续开发这些架构，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可追溯到1700年S的太阳黑子记录。这一序列的新见解来自于南澳大利亚前寒武纪时期形成的S岩石化石。值得注意的是，化石中的条纹宽度被认为构成了一千多年来太阳活动的记录。其形状和光谱特征与支撑系列基本一致。尽管这些数据显然很嘈杂，但它蕴含着丰富的信息。利用神经网络，PI将研究如何利用这一系列来改进我们目前对太阳黑子的预测。一般来说，如何利用不同时刻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。从实用的角度来看，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。

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.