2016
DOI: 10.2991/emcm-15.2016.72
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Nonlinear System Identification Based on Reduced Complexity Volterra Models

Abstract: Conventional Volterra series model is hardly applied to engineering practice due to its parametric complexity and estimation difficulty. To solve this problem, nonlinear system identification using reduced complexity Volterra models is proposed. Since the nonlinear components often play a secondary role compared to the dominant, linear component of the system, they spend the most of identification cost. So it is worth establishing a balance between identification cost and model accuracy by reducing the complex… Show more

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