CAJ | 학술논문

最小均方(Least Mean Square, LMS)算法的更新方向是对最速下降方向的估计，其收敛速度也受到最速下降法的约束。为了摆脱该约束，该文在对LMS算法分析的基础上，提出一种针对LMS算法的分块方向优化方法。该方法通过分析误差信号来选择更新向量，使得算法的更新方向尽可能接近Newton方向。基于此方法，给出一种方向优化LMS(Direction Optimization LMS, DOLMS)算法，并推广到变步长DOLMS算法。理论分析与仿真结果表明，该方法与传统分块LMS算法相比，有更快的收敛速度和更小的计算复杂度。
최소균방(Least Mean Square, LMS)산법적경신방향시대최속하강방향적고계，기수렴속도야수도최속하강법적약속。위료파탈해약속，해문재대LMS산법분석적기출상，제출일충침대LMS산법적분괴방향우화방법。해방법통과분석오차신호래선택경신향량，사득산법적경신방향진가능접근Newton방향。기우차방법，급출일충방향우화LMS(Direction Optimization LMS, DOLMS)산법，병추엄도변보장DOLMS산법。이론분석여방진결과표명，해방법여전통분괴LMS산법상비，유경쾌적수렴속도화경소적계산복잡도。
The update vector of Least Mean Square (LMS) algorithm is an estimation of the gradient vector, thus its convergence rate is limited by the method of steepest descent. Based on the discussion of basic LMS, a direction optimization method of LMS algorithm is proposed in order to get rid of this speed constraint. In the proposed method, the closest update vector to the Newton direction is chosen based on the analysis of the error signal. Based on the method, a Direction Optimization LMS (DOLMS) algorithm is proposed, and it is extended to the variable step-size DOLMS algorithm. The theoretical analysis and the simulation results show that the proposed method has higher speed of convergence and less computational complexity than traditional block LMS algorithm.