IEEE Transactions on Circuits and Systems Part I: Regular Papers
An improved particle swarm optimization algorithm for radial basis function neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IEEE Transactions on Neural Networks
Nonlinear system identification using two-dimensional wavelet-based state-dependent parameter models
International Journal of Systems Science
A forward regression algorithm based on M-estimators
CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
| Hi-index | 0.00 |
In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.