Blind identification of non-linear quadratic systems using higher order cumulants and non-Gaussian input signals

This work concerns the problem of the identification of the kernels of non-linear quadratic systems using cumulants of the output data corrupted by a Gaussian noise, when the input is a stationary zero mean non-Gaussian white stochastic process. The proposed approach constitutes an extension of linear systems identification algorithm to non-linear quadratic systems using third-order cumulants. The developed algorithm is tested and compared with a recursive least square and a least mean square methods using different quadratic models for various values of signal to noise ratio and different sample sizes N. The simulation results show the efficiency and the accuracy of the proposed algorithm in non-linear quadratic system identification.

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