引入松弛因子的高阶收敛FastICA算法

高阶收敛的FastICA算法对初始值的选择较为敏感，如果初始值选择不当不仅会影响算法的收敛效果，甚至可能导致不收敛的结果.针对这一问题，将松弛因子引入高阶收敛的牛顿迭代法中，通过适当的修正，获得了既能保证一定收敛速度，又能有效克服初值敏感性的改进三阶、五阶FastICA算法.仿真工具采用Matlab软件，应用3种算法对语音信号进行分离；结果表明，对比基本FastICA算法，改进后的算法有效地分离了混合信号，并且降低了算法对初始权值的依赖性.

Relaxation Factor based FastICA with Higher Order Convergence

High order FastICA(fast independent component analysis)algorithm has the characteristics of simple form and quick convergence. However， the algorithm is sensitive to its initial value which affects the convergence effect and even results in non convergence if it is not chosen appropriately. In order to solve this problem， a relaxation factor is introduced into high order Newton iterative method. Through the appropriate correction， the improved high order FastICA algorithm can be obtained， which can not only guarantee the convergence speed， but also effectively overcome the initial value sensitivity problem. Applying the algorithm to the separation experiment of speech signals， the result shows that the proposed algorithm effectively separates the mixed signal，and reduces the dependence on the initial value.