基于构造型神经网络的异常模式发现方法

Outlier Detection Method Based on Constructive Neural Networks

数据流的异常模式检测中,有时受噪声等因素影响发生概念漂移,影响了检测效率。针对此问题,提出一种基于构造型神经网络增量学习的异常模式动态检测方法,以提取滑动窗口内数据的数据概要,修正全局数据概要,更新已有的学习模型。另外,数据流速、流量等因素也影响检测效率,采用粒度分析思想改进检测方法,设置合适的时间滑动窗口,根据数据量自适应选择分析粒度,进而更准确地发现异常模式。无线电通信信号监测数据异常模式检测实验验证了本方法的有效性。

Outlier detection efficiency in data stream can be influenced by concept drift when there is noise. An outlier dynamic detection method based on constructive neural networks incremental learning was presented to solve this prob- lem. The outline of the data in the sliding window is acquired, and the learning model is modified. On the other hand, da- ta moving speed and flux can influence the efficiency as well. In this paper, the method of granular analysis was used to improve our method. The best analysis granularity in suitable sliding window was set to find outlier accurately. The simu- late experiment and the experiment of outlier detection in radio communication demonstrated the efficiency of this meth- od.