[PDF] Top 20 An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
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An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... An orthogonal least squares (OLS) algorithm including parameter regular- ization technique based on Gram-Schmidt orthogonal de- composition can be used to determine the significant model elements and ... See full document
6
Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... identification algorithm for linear-in-the-parameters ...the forward orthogonal least- square algorithm using the modified Gram – Schmidt ...Schmidt algorithm with basis ... See full document
8
M estimator and D optimality model construction using orthogonal forward regression
... Gram–Schmidt algorithm [6], in which least squares parameter estimates are usually ...Gram–Schmidt algorithm, a few variants of forward OLS algorithms have been introduced to improve model ... See full document
8
Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... of basis functions, aided by various state-of-the-art techniques ...linear-in-the-parameters regression models, which will be considered in the present study, are an important class of representations for ... See full document
10
Sparse multi output radial basis function network construction using combined locally regularised orthogonal least square and D Optimality experimental design
... 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 ... See full document
20
Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... of basis functions, aided by various state-of-the-art techniques ...linear-in-the-parameters regression models, which will be considered in this letter, are an important class of representations for ... See full document
6
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least ...proposed algorithm aims to ... See full document
18
Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation
... exactly k-sparse itself. Recent breakthrough results [CT05, Don06a, DT05a] showed that it is possible to construct measurement matrices with m = O(k log(n/k)) rows that recover k-sparse signals exactly in polynomial ... See full document
292
A forward regression algorithm based on M estimators
... above orthogonal decomposition can be realized by the modified Gram-Schmidt algorithm [3], in which least squares pa- rameter estimates are usually ...Gram-Schmidt algorithm, a few variants of ... See full document
5
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... [3] Chen, S., Hong, X., and Harris, C.J., 2002, “Sparse data modelling using combined locally regularized orthog- onal least squares and D-optimality design,” in: Proc. Com- bined Annual Conf. ... See full document
6
Modelling the Nonlinear Oscillations Due to Vertical Bouncing Using a Multi-Scale Restoring Force System Identification Method
... iOFR algorithm is again used to select the significant terms from the mixed term dictionary consists of polynomial and RBF candidate ...radial basis functions keep same however the scales of the RBF terms ... See full document
23
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... Radial basis function networks possess several attractive ...radial basis function (MSRBF) network has been introduced to model and forecast the Dst ...multiscale basis functions with different ... See full document
17
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... LROLS algorithm, the choice of is less critical than the original OLS ...in forward regression only affects the stopping point of the model selection, but does not penalizes the regressor that may ... See full document
8
An extended orthogonal forward regression algorithm for system identification using entropy
... OFR algorithm for the identification of both the model terms or structure and the unknown parameters of non-linear stochastic systems with Gaussian and non-Gaussian noise has been ...an optimality ... See full document
23
Forward Backward Synergistic Acceleration Pursuit Algorithm Based on Compressed Sensing
... in forward step and eliminates β atoms from the estimated support set in backward ...Acceleration Forward-Backward Pursuit (AFBP) algorithm, selected the high quality atoms again in backward ... See full document
10
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... OFR algorithm may include redundant autoregressive terms, even when the data set was produced from a purely moving average model (Piroddi and Spinelli, ...iOFR algorithm can correctly identify an optimal ... See full document
14
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... OLS algorithm based on PRESS statistic. For this example, the algorithm resulted in a sparse 22-term ...LROLS algorithm based on PRESS statistic, the model set contained 22 terms after the first ... See full document
14
Probability density function estimation using orthogonal forward regression
... a regression problem and the orthogonal forward regression tech- nique is adopted to construct sparse kernel density ...proposed algorithm incrementally minimises a leave-one- out test ... See full document
6
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... as illustrated in Fig. 3. At the upper level, the GA, with a population size of , learns the width and the regularization parameter based on the fitness function values provided by the lower level. The lower level ... See full document
5
Sparse Signal Reconstruction using Basis Pursuit Algorithm
... When each of the signal components admits a sparse representation with respect to known transforms, and when the transforms are sufficiently distinct, then the signal components can be isolated using sparse ... See full document
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