Add to ORKGThis paper presents a new model identification method for parsimoniously selecting model terms and estimating the corresponding parameters of nonlinear dynamical systems. The generalization and prediction capability of the final identified model with the smallest model size is ensured by optimizing the model prediction error over an unseen data using parametric bootstrap covariance estimates
This study presents a new algorithm for nonlinear rational model identification. The new algorithm c...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
This paper presents a new model identification method for parsimoniously selecting model terms and e...
A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonl...
It is known that the least-squares class of algorithms produce unbiased estimates providing certain ...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
A new regularised least squares estimation algorithm is derived for the estimation of nonlinear dyna...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
Abstract—In this correspondence new robust nonlinear model con-struction algorithms for a large clas...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A new adaptive orthogonal least squares (AOLS) algorithm is proposed for model subset selection and ...
Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in...
The objective of modelling from data is not that the model simply fits the training data well. Rathe...
This study presents a new algorithm for nonlinear rational model identification. The new algorithm c...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
This paper presents a new model identification method for parsimoniously selecting model terms and e...
A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonl...
It is known that the least-squares class of algorithms produce unbiased estimates providing certain ...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
A new regularised least squares estimation algorithm is derived for the estimation of nonlinear dyna...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
Abstract—In this correspondence new robust nonlinear model con-struction algorithms for a large clas...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A new adaptive orthogonal least squares (AOLS) algorithm is proposed for model subset selection and ...
Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in...
The objective of modelling from data is not that the model simply fits the training data well. Rathe...
This study presents a new algorithm for nonlinear rational model identification. The new algorithm c...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...