Nonparametric System Identification
نویسنده
چکیده
This article presents a survey of various methods for nonparametric identification of nonlinear systems. Nonparametric identification methods are those that measure Wiener kernels or Volterra kernels, since an output of a nonlinear system can be described by the convolution integral of Wiener or Volterra kernels and the system input. Section 1 highlights the representation methods of nonlinear systems by kernels including mutual relationships between Wiener kernels and Volterra kernels.
منابع مشابه
User choices for nonparametric preprocessing in system identification
Most research on system identification is focused on the identification of parametric models, for example a transfer function or a state space model where the information is condensed in a few parameters. In the daily practice, nonparametric methods, like frequency response function measurements, are intensively used. Recently, it was indicated that nonparametric identification methods could be...
متن کاملStochastic approximation in nonparametric identification of Hammerstein systems
Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented. Two of them recover the characteristic of the nonlinear memoryless subsystem while the third one estimates the impulse response of the linear dynamic part. The a priori information about both subsystems is nonparametric. Consistency in quadratic mean is shown and the convergen...
متن کاملNonparametric Identification of Nonlinear Oscillating Systems
The problem of system identification from a time series of measurements is solved by using nonparametric additive models. Having only few structural information about the system, a nonparametric approach may be more appropriate than a parametric one for which detailed prior knowledge is needed. Based on nonparametric regression, the functions in the additive models are estimated by a penalized ...
متن کاملIdentification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We ...
متن کاملUsing wavelet network in nonparametric estimation
Wavelet networks are a class of neural networks consisting of wavelets. In this paper, algorithms for wavelet network construction are proposed for the purpose of nonparametric regression estimation. Particular attentions are paid to sparse training data so that problems of large dimension can be better handled. A numerical example on nonlinear system identification is presented for illustration.
متن کامل