一种改进的个体激活因子比例仿射投影算法

An Enhanced Individual-Activation-Factor Proportionate Affine Projection Algorithm

基于变正规化参数的个体激活因子比例仿射投影算法(ERIAF-MPAPA)中将非活动系数的增益转换为活动系数的增益,但将增益与系数大小成正比,降低稳态过程中不活跃系数的收敛速度。针对此问题,提出一种改进的ERIAFMPAPA算法(MERIAF-MPAPA),该算法在自适应过程中使用一种新的增益分配策略来更新滤波器系数。该策略中当学习过程中跨越一个预定义的阈值时,将执行一个新的增益分配,对于几乎收敛的有效系数不再按照其大小成比例更新,而是用一个共同的最小增益更新,并允许对未达到收敛点的非活动效率的系数获得更高的收益。仿真结果表明:与ERIAF-MPAPA和其他比例仿射投影算法相比,该算法具有更快的收敛速度。

The individual-activation-factor memory proportionate affine projection algorithm with evolving regularization(ERIAF-MPAPA)that(dur⁃ing the adaptive process)uses a new gain distribution strategy for updating the filter coefficients.This strategy consists of increasing the gain assigned to the inactive coefficients as the active one approach convergence.For such,whenever a predefined threshold is crossed dur⁃ing the learning process,a new gain distribution is carried out,rather than to assign gains proportional to coefficient magnitudes as the EIAF-MPAPA algorithm does.This new version of the ERIAF-MPAPA algorithm(MERIAF-MPAPA)leads to a better distribution of the adaptation energy over the whole learning process.As a consequence,for impulse responses exhibiting high sparseness,the proposed algo⁃rithm achieves faster convergence,outperforming the ERIAF-MPAPA and other well-known PAPA-type algorithms.