An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D-optimality
نویسندگان
چکیده
A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified Gram-Schmidt procedure), followed by being tuned with a new gradient descent learning algorithm based on the basis pursuit that minimizes the norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression, so as to maintain computational efficiency. An illustrative example is included to demonstrate the effectiveness of the new approach.
منابع مشابه
Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality - Control Theory and Applications, IEE Proceedings-
An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm ba...
متن کاملFully complex-valued radial basis function networks: Orthogonal least squares regression and classification
We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Lik...
متن کاملSparse Multi-Output Radial Basis Function Network Construction Using Combined Locally Regularized Orthogonal Least Square and D-Optimality Experimental Design
A new construction algorithm for multi-output radial basis function (RBF) network modelling is introduce by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of pro...
متن کاملSparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental des - Control Theory and Applications, IEE Proceedings-
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of produci...
متن کاملRobust nonlinear model identification methods using forward regression
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 tha...
متن کامل