模糊Artmap与神经网络相结合的在线缺失输入处理方法
Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs with Missing Values
摘要：
提出了一种基于集成的方法来处理缺失数据，而不需要预测或估算缺失值。该技术适用于神经网络的在线运算，因而可用于在线状态监测。所提出的技术进行了测试，在分类和回归问题。模糊ARTMAPs的集成用于分类，而多层感知器的集成用于回归问题。使用这种集成为基础的技术得到的结果进行比较，使用自联想神经网络和遗传算法和研究结果的组合，这种方法可以执行高达9%更好的回归问题。所提出的技术的另一个优点是，它消除了寻找数据的最佳估计的需要，因此节省了时间。
英文摘要：
An ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an ensemble of multi-layer perceptrons is used for the regression problem. Results obtained using this ensemble-based technique are compared to those obtained using a combination of auto-associative neural networks and genetic algorithms and findings show that this method can perform up to 9% better in regression problems. Another advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
