Blind identification of second order Hammerstein series
Signal Processing
Three easy ways for separating nonlinear mixtures?
Signal Processing - Special issue on independent components analysis and beyond
Bibliography on cyclostationarity
Signal Processing
Blind equalization of a nonlinear satellite system using MCMC simulation methods
EURASIP Journal on Applied Signal Processing
Nonlinear system identification using neural networks trained with natural gradient descent
EURASIP Journal on Applied Signal Processing
MISEP Method for Postnonlinear Blind Source Separation
Neural Computation
gpICA: a novel nonlinear ICA algorithm using geometric linearization
EURASIP Journal on Applied Signal Processing
Particle filtering equalization method for a satellite communication channel
EURASIP Journal on Applied Signal Processing
Blind maximum likelihood identification of Hammerstein systems
Automatica (Journal of IFAC)
Blind maximum-likelihood identification of wiener systems
IEEE Transactions on Signal Processing
A post nonlinear geometric algorithm for independent component analysis
Digital Signal Processing
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A simple method is proposed for blind identification of discrete-time nonlinear models consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). The linear subsystems are allowed to be of nonminimum phase (NMP), though the first LTI can be completely identified only if it is of minimum phase. With a circularly symmetric Gaussian input, the linear subsystems can be identified using simple cepstral operations on a single 2-D slice of the N+1 th-order polyspectrum of the output signal. The linear subsystem of an LTI-ZMNL model can be identified using only a 1-D moment or polyspectral slice if it is of minimum phase. The ZMNL coefficients are not identified and need not be known. The order N of the nonlinearity can, in principle, be estimated from the output signal. The methods are analytically simple, computationally efficient, and possess noise suppression characteristics. Computer simulations are presented to support the theory