BCGs-FFT结合BP神经网络反演金属介质复合柱体目标参数  

Composite Metallic and Material Target Parameters Reconstruction by Means of BCGs-FFT and BP Neural Networks

作　　者：张清河[1] 邓小炼[1] 

机构地区：[1]三峡大学理学院,湖北宜昌443002

出　　处：《电子学报》2010年第5期1217-1220,共4页Acta Electronica Sinica

基　　金：国家自然科学基金(No.50679037)

摘　　要：利用双共轭梯度-快速傅里叶变换方法(BCGs-FFT)结合BP神经网络技术研究了金属介质复合结构柱体目标的电磁逆散射问题.先用BCGs-FFT方法计算了复合结构目标的正散射问题,得到不同目标参数下的多个观测点上的散射电场,以此作为训练样本提供给BP网络,经过适当的离线训练,再以新的散射电场作为网络的输入,实时重构了金属介质复合结构目标的几何、电磁参数.数值结果显示了该方法的有效性.In this paper, the stabilized biconjugate gradient fast Fourier transform method combined with a BP neural network technology is applied to the electromagnetic inverse scattering problem of composite metallic and m, aterial target. The first, the target scattered electric field is measured at some points by means of the BCGs-FFT,then the electric field scattered by the target are fed into the BP network, whose output are the electromagnetic parameter. After proper training, the inverse scattering model of target has been setup and the geometric and/or electromagnetic properties of composite metallic and material target are reconstructed in realtime. Numerical results are provided for the validation of the proposed approach.

关 键 词：复合结构 参数重构 双共轭梯度-快速傅里叶变换 BP神经网络 

分 类 号：O451[理学—无线电物理]