Title :
Two-stage series-based neural network approach to nonlinear independent component analysis
Author :
Gao, P. ; Khor, L.C. ; Woo, W.L. ; Dlay, S.S.
Author_Institution :
Sch. of Electr., Electron. & Comput. Eng., Newcastle upon Tyne Univ.
Abstract :
Linear independent component analysis (ICA) played an important role in the development of various signal processing techniques due to the inherent simplicity. However, the assumption of linear mixture is always violated in real life, which narrows down its applications. In this paper, the problem of nonlinear independent component analysis is considered. Based on a new type of nonlinear mixing model, we propose a two-stage series-based approach to recover the original source signals. The two-stage series-based algorithm offers significant advantages in terms of reduced computational complexity and better learning dynamics of the trajectory. Simulations have also been carried out to verify the efficacy of the proposed method
Keywords :
computational complexity; independent component analysis; neural nets; signal processing; computational complexity; linear mixture; neural network; nonlinear independent component analysis; nonlinear mixing model; original source signals; signal processing; two-stage series-based algorithm; Algorithm design and analysis; Computational complexity; Computational modeling; Independent component analysis; Neural networks; Nonlinear distortion; Nonlinear equations; Page description languages; Signal mapping; Signal processing;
Conference_Titel :
Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on
Conference_Location :
Island of Kos
Print_ISBN :
0-7803-9389-9
DOI :
10.1109/ISCAS.2006.1693644