基于径向基函数神经网络的线性衰减系数的重建算法

层析γ扫描(TGS)技术是非破坏性分析(NDA)中的一项重要技术.在TGS透射测量中,线性衰减系数值的图像重建问题是TGS的难点和核心问题.在文[1]的基础上,提出了将神经网络方法应用于TGS重建线性衰减系数图像的算法.计算机上的仿真模拟结果表明,在一定范围内,径向基函数(RBF)神经网络方法重建的线性衰减系数值与实际值的相对误差小于4%,且具有快速、高精度等优点,表明了此方法的有效性.

Reconstruction Algorithm of Radial Function Neural Networks for Linear Attenuation Coefficients

Tomographic Gamma scanning(TGS) technique is an important technique in nondestructive assay(NDA). The image reconstruction problem of linear attenuation coefficients is very difficult and central in transmission TGS. A new image reconstruction algorithm of linear attenuation coefficients with neural networks is proposed based upon paper [1]. Simulated results indicate that the reconstruction relative errors of linear attenuation coefficients are less than 4% for the reconstruction algorithm of radial basis function(RBF) neural networks and the new algorithm has the merits such as fast response and high accuracy within a certain scope.