Title :
Employing adaptive functions and maximum entropy principle for nonlinear blind source deconvolution
Author :
Corinti, E. ; Amadio, V. ; Tummarello, G. ; Piazza, F.
Author_Institution :
Dip. Elettronica e Autom., Polytechnic Univ. of Marche, Ancona, Italy
Abstract :
In this paper we present the results of applying adaptive nonlinearities and maximum entropy principle to identify an inverting filter for the post nonlinear blind source deconvolution problem. The filter is a cascade of a linear FIR matrix and a nonlinear memoryless componentwise system. Cubic splines and polynomials have been selected as adaptive parametric functions. A fast frequency implementation is also proposed.
Keywords :
blind source separation; deconvolution; filtering theory; filters; maximum entropy methods; nonlinear systems; splines (mathematics); adaptive functions; adaptive parametric functions; cubic splines; inverting filter; linear FIR matrix; maximum entropy principle; nonlinear blind source deconvolution; nonlinear memoryless componentwise system; polynomials; Adaptive filters; Biomedical signal processing; Blind source separation; Deconvolution; Entropy; Finite impulse response filter; Frequency; Nonlinear filters; Polynomials; Vectors;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223911
