中文摘要：

对于同时存在亚高斯和超高斯源信号的盲信号分离问题,提出了适用于两类信号同时存在的合成概率模型,并以此概率模型计算非线性函数得到了分离两类信号的统一算法。同时,引入模糊推理系统在线确定自适应算法的迭代步长,因此学习算法的收敛速度更快,而且稳态误差更小。仿真实验分别通过对语音信号以及人工合成信号的分离,验证算法的有效性,并通过计算分离信号的干扰信号比（interference-to-signal ratio,ISR）,证明提出的算法能够更好、更快地分离亚高斯与超高斯混合信号。

英文摘要：

The problem of simultaneous blind separation of sub-and super-Gaussian signals is addressed.Firstly,a mixture density model suitable for the case where these two types signals exist simultaneously is proposed.Then the nonlinear function is computed according to this density model.A unifying algorithm for separating these two type signals is obtained in the end.On the other hand,the fuzzy inference system to determine the step length of the proposed adaptive algorithm on line is applied.Therefore,the learning algorithm has faster convergence speed and smaller steady state error.Simulation experiments of separating phonetic signals and artificial synthetical signals demonstrate the validity of the proposed algorithm,and the interference-to-signal ratio（ISR） is calculated to describe the extension of the improvement of the proposed algorithm.