orthogonal least-squares
Orthogonal least squares regression with tunable kernels
... A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal ...
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Finite impulse response filter design using a forward orthogonal least squares algorithm
... There are many well developed FIR filter design methods. The fir2() routine is one method embedded in the MAT- LAB signal processing toolbox, which can be used to design frequency sampling-based FIR filters with ...
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Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... iterative orthogonal least squares forward regression (iOFR) algorithm is proposed to identify nonlinear systems which may not be persistently ...forward orthogonal regression (OFR) algorithm, ...
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Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... ized orthogonal least squares (LROLS) algorithm [10]–[12] has re- cently been proposed, which introduces an individual regularizer for each ...
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Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... of Squares) statistic and regularised orthogonal least ...regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... Simpler learning can often be achieved if a neural network has a linear-in-the-parameters structure. When the width pa- rameter is fixed and a set of RBF centers is provided, a RBF network has such a structure and an ...
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A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... This study introduces a new method for ranking significant variables and selecting a subset from a library consisting of all the original variables. In the new method, a general variable detection and subset selection ...
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Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... The orthogonal least squares regression(OLSR) algorithm [4] developed in the late 80s for nonlinear system modeling, remains popular for nonlinear data modeling, for the reason that the algorithm is ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... The orthogonal least squares (OLS) algorithm [9,11] is an efficient learning procedure for constructing sparse regression ...every orthogonal weights in the ...
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Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The proposed algorithm combines the advantages of both the orthogonal forward regression and regularization methods to provide an efficient and powerful procedure for constructing parsim[r] ...
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Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... An orthogonal least squares forward selection procedure is employed to append the regressors one by ...original least squares problem. Different from the least squares ...
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Model structure selection in powertrain calibration and control
... and orthogonal least squares techniques in Chapter ...thogonal least squares (OLS) techniques can be applied successfully to the torque and Air/Fuel ratio model ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... associated Orthogonal Forward Regression (OFR) algorithm (Billings, 2013) (also referred to as the OLS (Orthogonal Least Squares) or the FOLSR (Forward Orthogonal Least ...
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Image magnification by least squares surfaces
... This paper continues as follows. In the second part, quadratic surfaces and the theory of least squares will be discussed. In the third part, the least square planes, suggested algorithms, and ...
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Consistent least squares fitting of ellipsoids
... The OLS estimation of the ellipsoid parameters from noisy measurements of points on its boundary is a nonlinear least squares problem. An indirect, suboptimal approach was used that transforms the ellipsoid ...
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A novel interpretation of least squares solution
... We show that the well-known least squares LS solution of an overdetermined system of linear equations is a convex combination of all the non-trivial solutions weighed by the squares of t[r] ...
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Deformation analysis with Total Least Squares
... Recalling the transformed coordinates of the object points given in Table 6, the differences between the LS and TLS solution are in cm levels. Though, this level of difference is not very important for such a study area ...
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On weighted structured total least squares
... The equivalent optimization problem (1) is a nonlinear least squares prob- lem, so that classical optimization methods can be used for its solution. The optimization methods require a cost function and first ...
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Optimal Dictionary for Least Squares Representation
... Let us consider a dictionary consisting of (unit) vectors that are ‘close’ to each other, i.e., the inner product between any two elements of the dictionary is close to 1. It is quite evident that such a dictionary is ...
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Least squares approximations of power series
... classical least squares solutions in C[ − 1, 1] in terms of linear combinations of ul- traspherical polynomials are extended in order to estimate power series on ( − 1, ...
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