The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Nonlinear system identification using autoregressive quadratic models
Signal Processing
Time series nonlinearity modeling: a Giannakis formula type approach
Signal Processing - Special section: Hans Wilhelm Schüßler celebrates his 75th birthday
EURASIP Journal on Advances in Signal Processing
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
Nonlinear system identification: an effective framework based on the Karhunen-Loève transform
IEEE Transactions on Signal Processing
Blind identification of LTI-ZMNL-LTI nonlinear channel models
IEEE Transactions on Signal Processing
Linear multichannel blind equalizers of nonlinear FIR Volterrachannels
IEEE Transactions on Signal Processing
Hypothesis Testing for Nonlinearity Detection Based on an MA Model
IEEE Transactions on Signal Processing
On the convergence of Volterra filter equalizers using a pth-orderinverse approach
IEEE Transactions on Signal Processing
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Blind identifiability of a quadratic stochastic system
IEEE Transactions on Information Theory
Nonlinear spline adaptive filtering
Signal Processing
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In this paper, we develop two main results. The first one is a theorem proving that a second order Wiener model can be blindly identified, i.e. using only the mean, the third and fourth order cumulants of the output data. The second result is the application of this theorem to spectral inversion (i.e. the recovering of the power spectrum density) of the input signal of a second order Volterra model to which usual inversion schemes cannot be applied, in particular when the linear kernel has a strong attenuation in frequency range. Numerical results are discussed with respect to the nonlinear energy amount of the output, the time series length and the SNR values.