[PDF] Top 20 An extended orthogonal forward regression algorithm for system identification using entropy
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An extended orthogonal forward regression algorithm for system identification using entropy
... the algorithm is developed under the assumption that the involved variables are mutually independent or uncorrelated with a jointly Gaussian ...Shannon’s entropy power inequality for dependent variables ... See full document
23
Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... modelling using associative memory networks or fuzzy logic has been the problem of the curse of dimensionality ...inference system (ANFIS) [6], Takagi and Sugeno model [7], ...an orthogonal least ... See full document
8
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... OFR algorithm, the terms are selected into the model one at a ...iterative orthogonal Forward regression algorithm has been introduced to improve the suboptimal problem where a small ... See full document
14
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 ...nonlinear system in two different ...the system and generate ... See full document
23
Finite impulse response filter design using a forward orthogonal least squares algorithm
... The Orthogonal Least Squares (OLS) algorithm was derived as an effective solution for structure selection and parameter estimation in nonlinear system identification ...OLS algorithm to ... See full document
7
A forward regression algorithm based on M estimators
... model identification algorithm that combines M-estimator with forward regression is in- troduced based on the modified Gram-Schmidt ...the system output vector ... See full document
5
Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... formed using some given primary basis functions according to some specified ...of system identification involves two aspects: the selection of the significant model terms and the determination of the ... See full document
10
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 ...derived ... See full document
8
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 ... See full document
22
Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... models using some specific types 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 ... See full document
6
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... The system identification problem involves selecting the most significant terms from a pre-defined candidate dictionary to build a model which is sufficient to describe the observed system ... See full document
25
An iterative orthogonal forward regression algorithm
... associated Orthogonal Forward Regression (OFR) algorithm have been widely applied in nonlinear system identification including in the modelling of many engineering, chemical, ... See full document
28
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... estimates using an orthogonal forward regression (OFR) that incrementally minimizes the training mean square error (MSE) ...construction algorithm is computationally simple and ... See full document
10
An Enhanced Information Security System Using Orthogonal Codes and MRJTC
... Studies on image compression and steganography have been an active area of research from the beginning of the digital image processing. The use of preprocessing methods for improving compression rate and elevating the ... See full document
5
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based ... See full document
17
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... construction algorithm, called the LS-SESVM, has been ...selected using the standard OLS forward selection procedure and the corresponding model weights are then computed using the LS-ESVM ... See full document
12
SNR estimation using extended kalman filter technique for orthogonal frequency division multiplexing (OFDM) system
... Orthogonal Frequency Division Multiplexing (OFDM) have been invented for more than 40 years ago and is implemented in wide variety of applications in digital transmission system. OFDM has proven to be ... See full document
36
A sparse kernel density estimation algorithm using forward constrained regression
... is employed for parameter estimation. Although in general the jackknife param- eter estimator is regarded as computationally intensive, the additional computa- tion is minimal in the proposed algorithm. This is ... See full document
10
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... by using (15), in which no matrix inversion is ...the forward regression process via a recursive ...construction using the forward ... See full document
18
Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
... kernel regression modelling (both of SVM and OLS), each kernel regressor is positioned at a training input data point and a single common kernel variance is used for every ...regerssors. Using the OLS ... See full document
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