Abstract
This paper considers the identification of a Volterra system and its applications in structural identification of nonlinear block-oriented models. Any order of the Volterra output is estimated separately via multilevel excitations and optimizations. Then, each order of the Volterra kernels is estimated independently with improved accuracy. Finally, relationships between the first and the second order Volterra kernel functions of block-oriented models are exploited to determine the structures of nonlinear block-oriented systems. The simulation studies verify the effectiveness of the proposed Volterra series identification method and the structure identification method for nonlinear block-oriented systems.
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