基于RBF网络的欠定盲分离源信号恢复

基于优化近似(l0)范数的算法应用于欠定盲分离源信号恢复时,存在算法复杂度较高,恢复精度受步长影响较大的问题,为此,提出了基于径向基函数(RBF)网络的欠定盲分离源信号恢复算法.该算法借助RBF网络进行交替优化,同时引入修正牛顿法对最小化近似l0范数进行求解,避免了传统的近似(l0)范数重构算法因步长选择不当造成恢复精度较低的缺点.仿真结果表明,与现有的基于平滑(l0)范数的算法相比,所提方法在保证较高恢复精度的同时复杂度明显降低.

Source Recovery in Underdetermined Blind Source Separation Based on RBF Network

When the algorithms based on optimizing approximated l0 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 l0 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.