Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach
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Publication:3152488
DOI10.1080/00207720110045056zbMath1015.93013OpenAlexW2144964428MaRDI QIDQ3152488
Adam Krzyżak, Balázs Kégl, Jerzy Z. Sasiadek
Publication date: 30 July 2003
Published in: International Journal of Systems Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207720110045056
nonparametric identificationcorrelation methodHammerstein and Wiener systemsHermite series orthogonal expansions
Related Items (3)
Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems ⋮ Identification for Wiener systems with RTF subsystems ⋮ Identification for Wiener systems with internal noise
Cites Work
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- On nonparametric estimation of nonlinear dynamic systems by the Fourier series estimate
- The identification of nonlinear biological systems: LNL cascade models
- Fourier and Hermite series estimates of regression functions
- Properties of Hermite series estimation of probability density
- Recursive nonparametric identification of Hammerstein systems
- Structure identification of nonlinear dynamic systems - A survey on input/output approaches
- On estimation of a class of nonlinear systems by the kernel regression estimate
- Discrete time adaptive control of linear dynamic systems with a two-segment piecewise-linear asymmetric nonlinearity
- On Convergence Rates in Nonparametric Problems
- On the design of nonlinear discrete-time predictors (Corresp.)
- Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
- 2-D partial fraction expansions and minimal commutative realizations
- Signal identification after noisy nonlinear transformations
- Consistent estimation of continuous-time signals from nonlinear transformations of noisy samples
- Approximations for nonlinear functions
- Global convergence of the recursive kernel regression estimates with applications in classification and nonlinear system estimation
- A comparison of two Hammerstein model identification algorithms
- Identification of discrete Hammerstein systems by the Fourier series regression estimate
- Hammerstein system identification by non-parametric regression estimation
- On identification of block orientated systems by non-parametric techniques
- Estimation of Probability Density by an Orthogonal Series
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