基于遗传小波神经网络MIG焊熔透状态模式识别

Penetration state recognition of MIG welding based on genetic wavelet neural network

通过对已有的人工神经网络、小波分析、遗传算法的建模方法进行组合利用并加以改进,建立了基于电弧声信号特征的MIG焊熔透状态诊断网络模型.声波信号经小波去噪和小波包频带能量特征提取后,作为小波神经网络模型的输入特征向量,网络训练中采用具有全局优化能力的遗传算法动态修改网络结构和参数,避免了神经网络训练速度慢、容易陷入局部极值的缺点,从而完成数据挖掘和复杂的非线性建模功能.结果表明,将网络模型用于熔透状态诊断,证实了方案的可行性和有效性.

A network model for penetration state diagnosis based on the signal characteristics of arc sound in MIG welding is developed by recombining and improving artificial neural network,wavelet transform,and genetic algorithm.The arc sound signals,which are denoised by using wavelet transform and extracted by the frequency-band energy characteristics via wavelet packet decomposition and reconstruction,are used as the input eigenvectors of the wavelet neural network model,the genetic algorithm which has the ability of global optimization is adopted to dynamically modify the network structure and parameters and eliminate the rate tardiness of neural network training and relapse into local extremum,and then the complex nonlinear modeling and data mining are accomplished.The penetration state diagnosis result of the trained network model verifies the feasibility and validity of the modeling methods.