基于径向基函数神经网络的线性衰减系数的重建算法

Reconstruction Algorithm of Radial Function Neural Networks for Linear Attenuation Coefficients

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.

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. 