论文信息 - Nonparametric identification of a particular nonlinear time series system

Nonparametric identification of a particular nonlinear time series system

A nonlinear time series system is identified. The system has a cascade structure, that is, it consists of a nonlinear memoryless element followed by a dynamic linear system. Given a Gaussian input, a Hermite series based method for recovering the system nonlinearity is presented. The proposed identification procedure is nonparametric since it is able to be consistent for a broad class of nonpolynomial characteristics. The consistency and rate of convergence of the procedure are established. Also, some data-driven methods for selecting the optimal number of terms in the procedure are proposed. The results are illustrated by a numerical example. >

M. Pawlak | W. Greblicki

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