噪声自适应抑制技术在大坝安全监测数据处理中的应用

利用传感器监测大坝安全特征量从而实时掌握大坝安全状况是目前较为常见的安全监控手段。噪声干扰是传感器数据输出的重要问题,严重影响建模分析的精度。针对传统线性滤波的不足,提出了基于RBF神经网络的非线性神经网络滤波器,该模型克服了传统线性滤波对非高斯噪声处理时的缺点,且不需要关于输入信号和噪声的先验知识,非线性映射能力强。采用自适应噪声抵消基本原理,构造RBF神经网络自适应滤波器,然后针对该系统建立Simulink仿真模型。该技术应用在大坝监测数据处理中,取得了良好的效果。

Application of Adaptive Noise Canceling Technology in Data Processing of Dam Safety Monitoring

It＇s the usual safety monitoring method that sensors are used to get the dam safety status by monitor the eigenvalue of dam.Noise interference is the problem of data outputting by sensors,and it affects the precision of the modeling analysis.The traditional linear filtering has the lake of non-Gaussian noise,so the noise cancellation based on RBF neural network filtering is proposed.RBF neural network noise cancellation system does not need the previous information of input signal and noise and has better ability of nonlinear mapping.According to the theory of self-adaptive noise cancellation,RBF neural network adaptive filter is established and then Simulink Model is established for the system.This method is used in data processing,and the results show that the method has good performance in controlling noises.