【摘要】 提出一种适用于传感器网络应用的数据流异常时空综合检测与修正的方法,将基于小波的时域滤噪方法与基于BP神经网络的空域数据融合相结合,并在此基础上提出了基于小波尺度的异常检测与修正方法,通过时间阈值确定数据融合所采用的时间窗口,利用小波变换的时分和频分特性,将异常检测与修正和异常数据持续时间相联系,通过多传感器数据融合的结果对传感器网络异常数据进行修正,从而剔除传感器网络中的异常数据。更多还原

【Abstract】 In this article,a method that is applied to detect and correct abnormity data over data streams of sensor networks is proposed.The temporal denoising method based on wavelet transforms and the spatial data fusion method based on BP neural network are combined in the model.Based on this model,a detection and correction for abnormity data algorithm based on wavelet scale is proposed.The time window used in data fusing is determined by the time threshold in the algorithm.Using the time division and frequency division characters of wavelet transform,detection and correction for abnormity data is related to the sustaining time of abnormity data.The wrong report of abnormity data is gotten rid of by the correcting of the sensor networks abnormity data using the data fusing result of muti-sensors.更多还原