Abstract
We propose an independent component analysis (ICA) algorithm which can separate mixtures of sub- and super- Gaussian source signals with self-adaptive nonlinearities. The ICA algorithem in the framework of natural Riemannian gradient is derived using the parameterized Weibull density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Weibull density model. Computer simulation results confirm the validity and high performance of the proposed algorithm Key words: Independent component analysis, weibull distribution, maximum likelihood, sub- and super- Gaussian, blind signal separation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
