中国物理B ›› 2012, Vol. 21 ›› Issue (5): 50204-050204.doi: 10.1088/1674-1056/21/5/050204
王芳芳,张业荣
Wang Fang-Fang(王芳芳) and Zhang Ye-Rong(张业荣)†
摘要: A novel method based on the relevance vector machine (RVM) for the inverse scattering problem is presented in this paper. The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered. The nonlinearity is embodied in the relation between the scattered field and the target property, which can be obtained through the RVM training process. Besides, rather than utilizing regularization, the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output. Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy, convergence, robustness, generalization, and improved performance in terms of sparse property in comparison with the support vector machine (SVM) based approach.
中图分类号: (Inverse problems)