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Bias-compensated identification of quadratic Volterra system with noisy input and output

Bias-compensated identification of quadratic Volterra system with noisy input and output

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An iterative approach to identification of a quadratic Volterra system with noisy input-output is proposed, whereby the bias-compensated least-squares method of identifying a noisy FIR model is utilised with some modification to estimate input/output noise variances and bias-removed Volterra system parameters. In particular, the proposed identification approach yields better performance even in cases of fewer input/output data than conventional methods, and it can be also extended to identification of noisy higher-order Volterra systems.

References

    1. 1)
      • R. Diversi . A bias-compensated identification approach for noisy FIR models. IEEE Signal Process Lett. , 325 - 328
    2. 2)
      • T. Söderström , M. Hong , W.X. Zheng . Convergence properties of bias-eliminating algorithms for errors-in-variables identification. Int. J. Adapt. Control Signal Process , 703 - 722
    3. 3)
      • U. Ozertem , D. Erdogmus . Second-order Volterra system identification with noisy input-output measurements. IEEE Signal Process Lett. , 18 - 21
    4. 4)
      • T. Ogunfunmi , S.L. Chang . Second-order adaptive Volterra system identification based on discrete nonlinear Wiener model. IEEE Proc. Vis. Image Signal Process , 21 - 29
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