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Application of Self-Adaptive Wavelet Neural Networks in Ultrasonic Detecting of Drainpipe Drainpipe

机译:自适应小波神经网络在排水管超声检测中的应用

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Drainpipe ultrasonic non-destructive testing is liable to be interfered with the external environment So it is important to remove the noise signal effectively in drainpipe ultrasonic nondestructive testing. The testing system is constructed by selfadaptive wavelet neural networks which is using the wavelet and neural network algorithm. Better fitting signal is achieved by choosing Orthogonal Daubechies wavelet neuron and optimizing the scale parameter. The simulation results showed less distortion and better noise cancellation.
机译:排水管超声无损检测容易受到外界环境的干扰,因此有效地去除噪声信号在排水管超声无损检测中很重要。该测试系统由自适应小波神经网络构造而成,采用小波和神经网络算法。通过选择正交Daubechies小波神经元并优化比例参数,可以获得更好的拟合信号。仿真结果表明失真较小,噪声消除效果更好。

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