Improved Multiplicative Orthogonal-Group Based ICA for Separating Mixed Sub-Gaussian and Super-Gaussian Sources

摘要：

Recently, the fully-multiplicative orthogonal-group ICA (OgICA) neural algorithm has been proposed, which exploits the known principle of diagonalisation of a tensor of a warped networks outputs. Unfortunately, the algorithm is only able to separate sub-Gaussian source signals. To address this problem, the paper proposes an improved algorithm that adopts two nonlinearities and a flexible nonlinear model switching technique. The improved OgICA algorithm can instantaneously separate not only the mixture of pure sub-Gaussian source signals, but also the mixture of super-Gaussian and sub-Gaussian source signals. Besides, the algorithm has fast convergence speed and high separation performance. The validity and effectiveness of our proposed algorithm are confirmed through extensive computer simulations.

作者: Yalan Ye Zhi-Lin Zhang Shaozhi Wu Xiaobin Zhou

作者单位: Blind Source Separation Research Group School of Computer Science and Engineering, University of Ele School of Computer Science and Engineering, University of Electronic Science and Technology of China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 340-343

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）