%0 Journal Article
%A CHEN Jie-hu
%A FU Wei-hong
%A LIU Nai-an
%A NONG Bin
%T Source Recovery in Underdetermined Blind Source Separation Based on RBF Network
%D 2017
%R 10.13190/j.jbupt.2017.01.017
%J Journal of Beijing University of Posts and Telecommunications
%P 94-98
%V 40
%N 1
%X When the algorithms based on optimizing approximated <em>l</em><sub>0</sub> norm are applied to source recovery in underdetermined blind source separation, the complexity is high and the recovery accuracy is greatly affected by the step size. An algorithm for source recovery in underdetermined blind source separation based on radial basis function (RBF) network (SRRBF) was proposed in order to solve these problems. Depending on RBF network, an alternate optimization is performed in the method proposed. Additionally, the approximated <em>l</em><sub>0</sub> norm is optimized by modified Newton method to avoid inaccurate recovery caused by unsuited step size. Simulations verify that computational complexity of SRRBF is dramatically low while the recovery precision is high.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017.01.017