CAJ | 학술논문

独立成分分析（ICA）只需要知道源信号较少的先验知识（如统计独立性等），仅由观测信号便能恢复出源信号的特性，因而得到了广泛应用。ICA的目的是寻找变换矩阵，使输出信号经变换后各成分之间尽可能的统计独立，其关键是建立一个目标函数，使得最大化（或最小化）目标函数的解便是所要找的变换矩阵。首次将人工鱼群算法（AFSA）与ICA相结合，提出了基于AFSA的独立成分分析算法。以负熵极大化作为目标函数，通过人工鱼的觅食，聚群和追尾行为，更新人工鱼的位置，得到全局最优解，从而得到分离矩阵。与自然梯度法相比，鱼群算法精度更高，收敛速度更快，仿真实验表明了将鱼群算法应用于独立成分分析的可行性和有效性。
독립성분분석（ICA）지수요지도원신호교소적선험지식（여통계독립성등），부유관측신호편능회복출원신호적특성，인이득도료엄범응용。ICA적목적시심조변환구진，사수출신호경변환후각성분지간진가능적통계독립，기관건시건립일개목표함수，사득최대화（혹최소화）목표함수적해편시소요조적변환구진。수차장인공어군산법（AFSA）여ICA상결합，제출료기우AFSA적독립성분분석산법。이부적겁대화작위목표함수，통과인공어적멱식，취군화추미행위，경신인공어적위치，득도전국최우해，종이득도분리구진。여자연제도법상비，어군산법정도경고，수렴속도경쾌，방진실험표명료장어군산법응용우독립성분분석적가행성화유효성。
Independent Component Analysis(ICA)which requires a little prior knowledge(such as independent)of signals is widely applied. The goal of ICA is to find a separation matrix so that each component of the output signal by transforming is independent. The key of ICA is to construct a target function, and then obtain the separation matrix by maximize(or minimize) the target function. This paper proposes an ICA algorithm based on Artificial Fish Swarm Algorithm(AFSA). With the target function of maximum negentropy, it can obtain the separation matrix through foraging, cluster and tracing behavior of artificial fishes and updating artificial fish position. Compare with natural gradient, AFSA has the high accuracy and fast convergence rate. Experimental results are provided to evaluate the performance of the proposed algorithm.