Title :
Blind source separation of nonlinear mixing models
Author :
Lee, Te-Won ; Koehler, Bert-Uwe ; Orglmeister, Reinhold
Author_Institution :
Salk Inst., San Diego, La Jolla, CA, USA
Abstract :
We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms
Keywords :
Jacobian matrices; learning (artificial intelligence); maximum entropy methods; neural nets; polynomials; signal processing; transfer functions; blind source separation; convergence; higher order polynomials; information maximization criterion; learning rules; linear mixing; nonlinear mixing models; nonlinear transfer channel; parametric sigmoidal nonlinearity; Blind source separation; Brain modeling; Electroencephalography; Independent component analysis; Neurons; Polynomials; Signal analysis; Signal processing algorithms; Speech; Transfer functions;
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
Print_ISBN :
0-7803-4256-9
DOI :
10.1109/NNSP.1997.622422
