CISE Postdoctoral Program: Robust Forecasting with Neural Networks

CISE博士后项目：神经网络稳健预测

• 批准号: 9503968
• 负责人: John Moody
• 金额: $ 4.62万
• 依托单位: Oregon Graduate Institute of Science & Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-03-15 至 1997-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9503968&HistoricalAwards=false
• 关键词: CISE; Postdoctoral; Program; Robust; Forecasting

9503968 Moody In this project robust artificial neural network (ANN) learning algorithms and tools for forecasting time series, with specific applications to financial and economic time series will be developed. These algorithms and tools are difficult to predict due to the non-normal distribution of the noise and the presence of outliers, as well as the nonstationarity of both noise and signals. They thus make excellent benchmark problems for robust nonlinear modeling techniques. In robust models, robust error measures for gracefully handling outliers in the data will be incorporated. Also, a new method for automatically regularizing (constraining) the ANN model during training by using second order statistics of the cost function gradient. This prevents the network from adapting to noise and thus produces robust models. In the area of learning algorithms, the application of a new simulated annealing type learning algorithm to recurrent networks will be investigated. It is believed that, based upon past experience with feedforward networks with many hidden layers, this algorithm will improve the performance of recurrent networks. In variable selection, a novel use of genetic algorithms (GA) in combination with ANN models to select optimal subsets of variables for solving a problem will be pursued. Combining GAs with a newly developed statistical test, the so-called delta-test, into a more powerful tool for determining the optimal variable subset will also be done. ***

小行星9503968 在这个项目中，强大的人工神经网络（ANN）的学习算法和工具，预测时间序列，具体应用于金融和经济时间序列将开发。 由于噪声的非正态分布和异常值的存在，以及噪声和信号的非平稳性，这些算法和工具难以预测。 因此，他们为强大的非线性建模技术的优秀基准问题。 在稳健模型中，将纳入稳健的误差测量，以妥善处理数据中的离群值。 此外，一种新的方法，用于自动正则化（约束）的ANN模型在训练过程中，通过使用二阶统计的成本函数梯度。 这可以防止网络适应噪声，从而产生鲁棒的模型。 在学习算法方面，将研究一种新的模拟退火型学习算法在递归网络中的应用。 据信，根据过去的经验，前馈网络与许多隐藏层，这种算法将提高递归网络的性能。 在变量选择方面，将采用遗传算法与人工神经网络模型相结合的新方法来选择解决问题的最佳变量子集。 将遗传算法与新开发的统计检验（所谓的δ检验）相结合，成为一个更强大的工具，用于确定最佳变量子集。 ***