  Blind source separation(BSS) is a hotspot in signal processing, and
independent component analysis (ICA) is a very effective tool for solving the
BSS problem. In order to improve the performance of the separation, a new
nonlinear function sin was introduced. It can replace the commonly used
classical functions (tanh, gauss and pow3) and does not need to select
different nonlinear functions according to the Gauss property of signals. The
two Matlab simulation results show that the improved Fast-ICA algorithm with
the proposed nonlinearity can not only improve the separation accuracy but also
speed up the convergence of blind source separation.
