id: 06098500
dt: j
an: 06098500
au: Ren, Dongxiao; Ye, Mao
ti: Extracting post-nonlinear signal with reference.
so: Comput. Electr. Eng. 37, No. 6, 1171-1181 (2011).
py: 2011
pu: Elsevier Science Ltd. (Pergamon), Oxford
la: EN
cc: 
ut: 
ci: 
li: doi:10.1016/j.compeleceng.2011.06.002
ab: Summary: Constrained independent component analysis (cICA) is an important
    technique which can extract the desired sources from the mixtures. The
    post-nonlinear (PNL) mixture model is more realistic and accurate than
    the linear instantaneous mixture model in many practical situations. In
    this paper, we address the problem of extracting the desired source as
    the first output from the PNL mixture. The prior knowledge about the
    desired source, such as its rough template (reference), is assumed to
    be available. Two approaches of extracting PNL signal with reference
    are discussed. Then a novel algorithm which alternately optimizes the
    contrast function and the closeness measure between the estimated
    output and the reference signal is proposed. The inverse of the unknown
    nonlinear function in the PNL mixture model is approximated by the
    multi-layer perception (MLP) network. The correctness and validity of
    the proposed algorithm are demonstrated by our experiment results.
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