Identification of discrete Volterra series using maximum length sequences

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Identification of discrete Volterra series using maximum length sequences

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An efficient method is described for the determination of the Volterra kernels of a discrete nonlinear system. It makes use of the Wiener general model for a nonlinear system to achieve a change of basis. The orthonormal basis required by the model is constructed from a modified binary maximum sequence (MLS). A multilevel test sequence is generated by time reversing the MLS used to form the model and suitably summing delayed forms of the sequence. This allows a sparse matrix solution of the Wiener model coefficients to be performed. The Volterra kernels are then obtained from the Wiener model by a change of basis.

Inspec keywords: discrete systems; binary sequences; nonlinear systems; Volterra series; identification; stochastic processes; sparse matrices

Other keywords: multilevel test sequence; Wiener general model; identification; discrete nonlinear system; modified binary maximum sequence; maximum length sequences; time reversal; Volterra kernels; Wiener model coefficients; discrete Volterra series; orthonormal basis; sparse matrix solution; delayed forms

Subjects: Nonlinear control systems; Discrete control systems; Simulation, modelling and identification; Other topics in statistics

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