南京大学学报(自然科学版)
南京大學學報(自然科學版)
남경대학학보(자연과학판)
JOURNAL OF NANJING UNIVERSITY
2009年
1期
83-88
,共6页
自适应均衡%定长或可变步长%均方误差准则%集粒子云算法
自適應均衡%定長或可變步長%均方誤差準則%集粒子雲算法
자괄응균형%정장혹가변보장%균방오차준칙%집입자운산법
adaptive equalization%fixed or variable step-size%MSE criterion%ensemble particle swarm algorithm
为了更加有效地对符号间干扰进行自适应补偿,本文提出了一种基于均方误差准则的集粒子云算法EPSA(ensemble particle swarm algorithm).在本算法中,自适应均衡器的每个抽头权值向量被看作问题空间中的一个粒子,所有权值向量对应于粒子云.根据均方误差准则,集粒子云算法以多维问题空间中飞行的粒子位置的集平均误差作为适应值,评估对应抽头权值向量的性能,从而改变粒子飞行的方向和速度,调整粒子位置的变化量.粒子在空中飞行由"互飞行"和"自飞行"构成,其中"互飞行"是通过粒子间的相互作用表征的,而"自飞行"则是通过粒子的自我调整实现的.粒子按各自的加速系数依次在不同模式中飞行,由于粒子间彼此互惠作用的影响,集粒子云算法的收敛速度得到了提高.理论分析和仿真结果均表明,本文提出的算法性能明显优于定步长或变步长的LMS算法,同时算法的复杂度几乎没有明显的增长.
為瞭更加有效地對符號間榦擾進行自適應補償,本文提齣瞭一種基于均方誤差準則的集粒子雲算法EPSA(ensemble particle swarm algorithm).在本算法中,自適應均衡器的每箇抽頭權值嚮量被看作問題空間中的一箇粒子,所有權值嚮量對應于粒子雲.根據均方誤差準則,集粒子雲算法以多維問題空間中飛行的粒子位置的集平均誤差作為適應值,評估對應抽頭權值嚮量的性能,從而改變粒子飛行的方嚮和速度,調整粒子位置的變化量.粒子在空中飛行由"互飛行"和"自飛行"構成,其中"互飛行"是通過粒子間的相互作用錶徵的,而"自飛行"則是通過粒子的自我調整實現的.粒子按各自的加速繫數依次在不同模式中飛行,由于粒子間彼此互惠作用的影響,集粒子雲算法的收斂速度得到瞭提高.理論分析和倣真結果均錶明,本文提齣的算法性能明顯優于定步長或變步長的LMS算法,同時算法的複雜度幾乎沒有明顯的增長.
위료경가유효지대부호간간우진행자괄응보상,본문제출료일충기우균방오차준칙적집입자운산법EPSA(ensemble particle swarm algorithm).재본산법중,자괄응균형기적매개추두권치향량피간작문제공간중적일개입자,소유권치향량대응우입자운.근거균방오차준칙,집입자운산법이다유문제공간중비행적입자위치적집평균오차작위괄응치,평고대응추두권치향량적성능,종이개변입자비행적방향화속도,조정입자위치적변화량.입자재공중비행유"호비행"화"자비행"구성,기중"호비행"시통과입자간적상호작용표정적,이"자비행"칙시통과입자적자아조정실현적.입자안각자적가속계수의차재불동모식중비행,유우입자간피차호혜작용적영향,집입자운산법적수렴속도득도료제고.이론분석화방진결과균표명,본문제출적산법성능명현우우정보장혹변보장적LMS산법,동시산법적복잡도궤호몰유명현적증장.
An effective ensemble particle swarm algorithm based on mean-squared error (MSE) criterion is proposed to compensate inter-symbol interference (ISI) adaptively. The position of each particle in the swarm, which is flying through the multidimensional problem space, is corresponding to a tap weight vector candidate. Based on MSE, each position is scored to obtain an ensemble average error as its fitness value. According to these values, the performance of the corresponding tap weight vector is evaluated and the direction and the velocity of each particle are adjusted so as to modify the increment of each particle's position. The flying of the particles consists of two different flying patterns, mutual flying and self flying, which are distinguished by respective acceleration coefficients. Mutual flying is characterized by the interaction of the particles, while self flying is performed by each particle itself. Theoretical analysis and computer simulations prove that the new algorithm achieves better performance with no observable increase of complexity compared with the LMS method with fixed or variable step-size.