The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
System identification: theory for the user
System identification: theory for the user
Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Advanced lectures on machine learning
Approximation algorithms for clustering uncertain data
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maximum likelihood identification of Wiener models
Automatica (Journal of IFAC)
Learning from dependent observations
Journal of Multivariate Analysis
Blind maximum-likelihood identification of wiener systems
IEEE Transactions on Signal Processing
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
Optimal learning rates for least squares regularized regression with unbounded sampling
Journal of Complexity
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Strong consistence of recursive identification for Wiener systems
Automatica (Journal of IFAC)
An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
IEEE Transactions on Information Theory
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
Regularization networks: fast weight calculation via Kalman filtering
IEEE Transactions on Neural Networks
On the estimation of transfer functions, regularizations and Gaussian processes-Revisited
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
Wiener system identification has been recently performed by adopting a Bayesian semiparametric approach. In this framework, the linear system entering the first block is given a finite-dimensional parametrization, while nonparametric Gaussian regression is used to estimate the static nonlinearity in the second block. In this paper, we study the asymptotic behavior of this estimator when the number of noisy output samples tends to infinity without assuming the correctness of the Bayesian prior models. For this purpose, we interpret Wiener identification under a machine learning perspective. This allows us to extend recent results on function estimation in reproducing kernel Hilbert spaces to derive a condition guaranteeing the statistical consistency of the identification procedure. We also discuss how the violation of such a condition can lead to useless estimates of the Wiener structure.