Nonlinear blind identification with three-dimensional tensor analysis.(English)Zbl 1264.93252

Summary: This paper deals with the analysis of a third-order tensor composed of a fourth-order output cumulants used for blind identification of a second-order Volterra-Hammerstein series. It is demonstrated that this nonlinear identification problem can be converted in a multivariable system with multiequations having the form of $$Ax + By = c$$. The system may be solved using several methods. Simulation results with the Iterative Alternating Least Squares (IALS) algorithm provide good performances for different signal-to-noise ratio (SNR) levels. Convergence issues using the reversibility analysis of matrices $$A$$ and $$B$$ are addressed. Comparison results with other existing algorithms are carried out to show the efficiency of the proposed algorithm.

MSC:

 9.3e+13 Identification in stochastic control theory
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References:

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