【摘要】 针对漏磁检测中的缺陷反演重构问题,引入了一种新型启发式优化算法—布谷鸟搜索算法,提出了以径向基函数神经网络为前向模型,布谷鸟搜索算法用作迭代算法的漏磁反演方法.为验证该反演方法的有效性,分别使用了不含噪声和含噪声的漏磁仿真信号以及实测漏磁信号.实验结果表明,与粒子群优化算法和差分进化算法相比,布谷鸟搜索算法的处理误差最小,而且对含噪声仿真漏磁信号和实测漏磁信号的重构结果依然能够较好地逼近真实缺陷.因此,基于布谷鸟搜索算法的反演方法对噪声具有一定的鲁棒性,是一种有效可行的漏磁反演方法.更多还原

【Abstract】 A new heuristic optimization method—cuckoo search( CS) algorithm is introduced and applied to inversing method of magnetic flux leakage( MFL). Radial basis function neural network is regarded as forward model and cuckoo search algorithm is used as iterative algorithm,thus the inversing method is proposed. Simulated MFL signals without noise,with noise and real MFL signals are used to verify the effectiveness of the inversing method,respectively. Experimental results proved the processing error of CS algorithm is smallest by comparing with particle swarm optimization algorithm and differential evolution algorithm. With existence of certain noise in MFL signal,defect profiles reconstructed by the proposed method are still close to the true profiles. The inversing method based on CS algorithm has robustness to the noise and is an efficient reconstructing method.更多还原