计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
209-212,270
,共5页
快速独立分量分析(FastICA)算法%有限支持样本核函数(FSS-kernel)算法%盲源分离%算法融合
快速獨立分量分析(FastICA)算法%有限支持樣本覈函數(FSS-kernel)算法%盲源分離%算法融閤
쾌속독립분량분석(FastICA)산법%유한지지양본핵함수(FSS-kernel)산법%맹원분리%산법융합
Fast Independent Component Analysis(FastICA)algorithm%Finite Support Sample(FSS)-kernel algorithm%blind source separation%algorithm fusion
FastICA算法有着比传统ICA算法更快、更稳健的收敛速度，但由于其选用的非线性函数不能很好地符合源信号的统计特性，恢复结果并不理想。针对该问题，提出了一种有限支持样本核函数（FSS-kernel）与FastICA融合的盲源分离算法。该方法是通过FSS-kernel算法估计得出源信号概率密度函数，结合FastICA算法，实现混合信号的盲分离。仿真结果表明，该方法能够有效地完成混叠信号的分离，通过与传统ICA算法及FastICA算法比较，证明了该方法具有更高的分离精度和自适应能力。
FastICA算法有著比傳統ICA算法更快、更穩健的收斂速度，但由于其選用的非線性函數不能很好地符閤源信號的統計特性，恢複結果併不理想。針對該問題，提齣瞭一種有限支持樣本覈函數（FSS-kernel）與FastICA融閤的盲源分離算法。該方法是通過FSS-kernel算法估計得齣源信號概率密度函數，結閤FastICA算法，實現混閤信號的盲分離。倣真結果錶明，該方法能夠有效地完成混疊信號的分離，通過與傳統ICA算法及FastICA算法比較，證明瞭該方法具有更高的分離精度和自適應能力。
FastICA산법유착비전통ICA산법경쾌、경은건적수렴속도，단유우기선용적비선성함수불능흔호지부합원신호적통계특성，회복결과병불이상。침대해문제，제출료일충유한지지양본핵함수（FSS-kernel）여FastICA융합적맹원분리산법。해방법시통과FSS-kernel산법고계득출원신호개솔밀도함수，결합FastICA산법，실현혼합신호적맹분리。방진결과표명，해방법능구유효지완성혼첩신호적분리，통과여전통ICA산법급FastICA산법비교，증명료해방법구유경고적분리정도화자괄응능력。
FastICA algorithm has a faster and more robust convergence speed than the traditional ICA algorithm. But its recovery results are not satisfied. Because the specified non-linear function is not well in line with the statistical properties of the source signal. In order to solve the problem, a novel blind source separation method based on FSS-kernel and FastICA combination is proposed in this paper. Probability density function of the source signal is estimated by the FSS-kernel algorithm, and then, to restore the blind separation of mixed signals, FastICA algorithm is used, the negative entropy is the objective function. The simulation results show that the signal aliasing could be separated effectively by this method. It is proved that the method has higher separation accuracy and adaptive capacity, by contrasting with the traditional ICA algorithms and FastICA algorithm.
