iterative orthogonal forward regression
An iterative orthogonal forward regression algorithm
... 2 Mao and Billings 1997; Piroddi and Spinelli 2003; Sherstinsky and Picard 1996). Solutions are available which solve this problem (Billings 2013; Billings and Wei 2007; Li et al. 2006; Mao and Billings 1997; Wei and ...
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Identification of continuous-time models for nonlinear dynamic systems from discrete data
... (iterative Orthogonal Forward Regression – Modulating Function) algorithm is proposed to identify continuous time models from noisy data by combining the modulating function method and the ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... (iterative Orthogonal Forward Regression) algorithm has therefore been proposed to reduce these problems while maintaining the simplicity of the identification ...squares regression ...
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Modelling the Nonlinear Oscillations Due to Vertical Bouncing Using a Multi-Scale Restoring Force System Identification Method
... Human vertical bouncing motion is studied using a system identification method. A multi-scale mathematical model is identified directly from real experimental data to characterise the nonlinear oscillation associated ...
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Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... new iterative orthogonal Forward regression algorithm which was introduced in the previous section was employed to overcome the problem by searching the optimal solutions on different ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... the forward selection principle adopted by the LROLS ...that forward selection is computationally more attractive compared with backward ...the iterative procedure converges ...the iterative ...
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Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... the forward orthogonal least- square algorithm using the modified Gram – Schmidt ...each forward regression step, with the basis pursuit that minimises the l 1 norm of the parameter estimates ...
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Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... linear-in-the-parameters regression problem, and involves a combination of a forward orthog- onal regression procedure and the calculation of mutual ...sparse regression model, where both the ...
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M estimator and D optimality model construction using orthogonal forward regression
... new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter ...an iterative reweighted ...
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Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... the regression model is the key feature of the RVM method and is ultimately responsible for the sparsity properties of the RVM method ...the iterative optimization ...
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A forward regression algorithm based on M estimators
... The orthogonal forward regression (OFR) is an efficient algorithm to determine a parsimonious model structure ...in forward regression ...an iterative reweighted least squares ...
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Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... SVM-based regression modelling techniques is the fact that the kernel centres or mean vectors are typically placed at the training input data and a fixed common kernel variance is used for all the regressor ...
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Building Regression Models with the Forward Search
... for regression do not ...likely; regression assumes, at least approximately, con- stant error variance, but here the non-negative responses range from 2 to ...
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Reverse Iterative Volume Sampling for Linear Regression
... 2013), but they require all of the responses from the original problem to generate the sketch and are thus not suitable for the goal of using as few response values as possible. The second approach is based on ...
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An Improved Orthogonal Iterative Algorithm for Monocular Camera Pose Estimation
... computational complexity of each iteration is O (1) , the computation of the iterative process can be greatly reduced. After the iteration, its easy to calculate the translation vector t .At the same time, its ...
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Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... This study aims to propose a new direct approach for identifying a mathematical model for the magnetospheric dynamics without any a priori information of the physical processes of the magnetosphere system but only a ...
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Orthogonal Least Square with Boosting for Regression
... A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of ...
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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 covariance ...
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Quantize-and-Forward Schemes for the Orthogonal Multiple-Access Relay Channel
... the iterative information bottleneck algorithm [17], where our algorithm can be recovered by choosing the Lagrange parameter β of [17] to be β ≫ 0 to ensure that the mapping p(z|ℓ) ∈ {0, 1} corresponds to a ...
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A Technique COFDM For Improving Efficiency To Transmitted An Images Through Modulation Technique For Wireless Communication Problem
... Abstract- The GSM & CDMA technologies has been widely used in wireless 3G system, But today 4G system wireless transmitter and receiver are used in communication. In this paper we presents novel method for used ...
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