Add to ORKGAbstract: In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation e±ciency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally e±cient. A numerical example is included to demonstrate e®ectiveness of the algorithms. Copyright c°2003 IFA
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
A general criterion is proposed for robust identification of both linear and bilinear systems. Follo...
In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
Abstract—In this correspondence new robust nonlinear model con-struction algorithms for a large clas...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual ...
This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual ...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
In this paper, estimation and identification theories will be examined with the goal of determining ...
Empirical or data-based modeling, generally referred to as system identification, plays an essential...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
A general criterion is proposed for robust identification of both linear and bilinear systems. Follo...
In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
Abstract—In this correspondence new robust nonlinear model con-struction algorithms for a large clas...
In this correspondence new robust nonlinear model construction algorithms for a large class of linea...
This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual ...
This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual ...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
In this paper, estimation and identification theories will be examined with the goal of determining ...
Empirical or data-based modeling, generally referred to as system identification, plays an essential...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
A general criterion is proposed for robust identification of both linear and bilinear systems. Follo...