基于遗传优化算法的二维漏磁缺陷重构

信号逆问题,即从测量信号中恢复出缺陷轮廓及其参数,是漏磁无损评估中的一个重要课题。提出了一种基于遗传算法的逆算法,用于从漏磁信号中重构二维缺陷。在该算法中,径向基函数（RBF）神经网络用作前向模型,遗传算法用于求解逆问题中的优化问题,其优点是能够避免基于梯度下降法的迭代逆算法中可能遇到的局部最小问题,并能得到逆问题的全局最优解。实验结果验证了所提出的逆算法的有效性。

2-D defect reconstruction from magnetic flux leakage signals based on a genetic optimization algorithm

In magnetic flux leakage(MFL) nondestructive evaluation,a crucial problem was signal inversion,i.e.,the defect profile and its parameters are recovered from measured signals.An inverse algorithm based on genetic algorithm was proposed for reconstructing 2-D defect from MFL signals.In the algorithm,radial-basis function(RBF) neural network was utilized as forward model,and genetic algorithm was used to solve the optimization problem in the inverse problem,which can avoid the local minimum solution possibly encountered in the iterative inverse algorithm based on gradient descent and consequently achieve the global optimized solution of the inverse problem.Experimental results verify the validity of the proposed inversion algorithm.