Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model

Zhang, Shiguang; Liu, Chao; Zhou, Ting; Sun, Lin

doi:10.1109/access.2020.2995615

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…By introducing the idea of twin support vector machines into the regression problem [35], [36], Peng proposed εtwin support vector regression(ε-TSVR) [37], and in the case of linear problems, ε-TSVR considers finding a pair of linear functions like in(12)…”

Section: B Twin Support Vector Regression(tsvr) Theorymentioning

confidence: 99%

Time-Delay Twin Support Vector Regression- Based Adaptive Predistorter for LED Nonlinearity in Visible Light Communications

et al. 2023

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The bit error rate (BER) of visible light communications (VLC) based on light emitting diodes (LEDs) has been limited due to the nonlinearity of LED. Artificial neural networks (ANNs) predistorters were applied to mitigate the impacts of LED nonlinearity. However, none of them can efficiently approach the performance of linear VLC systems due to the overfitting issue. In this paper, we for the first time propose a predistorter with adaptive indirect learning architecture based on amplitude timedelay twin support vector regression (ATD-TSVR) for LED nonlinearity in orthogonal frequency division multiplexing(OFDM)-based VLC systems. The experimental results demonstrate the signal distortion caused by LEDs as the major nonlinear source. Moreover, the simulation results show superior BER, amplitude distortion (AM/AM) curves, Power Spectral Density(PSD) and constellation plots in an 80Mbit/s OFDM nonlinear VLC system. Meanwhile, as compared to the traditional SVR approach, the CPU training time of ATD-TSVR can be reduced by more than four times. The adaptive pre-distortion method herein is generally applicable to broadband VLC systems and also proves the application prospect and effectiveness of TSVR in VLC system

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Section: B Twin Support Vector Regression(tsvr) Theorymentioning

confidence: 99%

Time-Delay Twin Support Vector Regression- Based Adaptive Predistorter for LED Nonlinearity in Visible Light Communications

et al. 2023

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“…Likewise, Formula (4) can be applied to obtain the upper bound prediction function of the TLSSVR model for any new test samples such as Formula (7).…”

Section: Twin Least Squares Support Vector Regressionmentioning

confidence: 99%

“…Secondly, the SVM model abides by the principle of structural risk minimization, rather than empirical risk minimization, thereby restricting the maximum generalization error.Traditional SVM faces challenges, including high computational complexity. To address this issue, various approaches can be taken, such as speeding up the training process with techniques like blocking, sequence minimization (SMO), SVMlight, SVMTORCH, and LIBSVM [6][7][8][9]. Alternatively, SVM models can be modified, such as using simplified Support Vector Machine, linear programming Support Vector Machine , or least squares Support Vector Machine (LSSVM) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Vegetable price forecast based on improved WTLSSVR model

Huang,

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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This article presents the implementation of the sparse twin least squares support vector regression (WTLSSVR) model, which is derived from the twin least squares support vector regression (TLSSVR) model. The WTLSSVR model achieves sparsity by eliminating sample points with small Lagrange multipliers.Our algorithm involves combining the LSSVR with the twin hyperplane algorithm, removing sample points with small Lagrange multipliers, and thinning the model to construct a sparse twin least squares support vector regression model. This approach enhances the predictive accuracy of current twin regressors, while maintaining excellent generalization performance.Therefore, the WTLSSVR is superior to the traditional LSSVR in generalization performance. In addition, compared with some other traditional algorithms, WTLSSVR has higher prediction accuracy, which is obvious when dealing with large-scale problems. To validate the proposed model’s soundness and practicality, we carried out numerous experiments, encompassing two UCI datasets and a genuine vegetable price dataset. The results of the experiments indicate that the suggested algorithm is efficacious, practical, and versatile.

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“…Later, Zhao et al [21] introduced the concept of least squares into TSVR and constructed twin least squares support vector machines (TLSVR). Zhang et al [22], [23] constructed a new model by combining Gauss-Laplace mixed noise distribution and heteroscedasticity Gaussian distribution with TLSVR, and the experimental results show that its prediction performance is better.…”

Section: Introductionmentioning

confidence: 99%

Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application

et al. 2022

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The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. Classic SVR, TSVR is assuming that accords with mean zero, variance of noise with the variance of the gaussian distribution or not to consider the effects of noise, but in some practical applications, such as wind speed forecasting, noise characteristic is more in line with mean zero, variance for heteroscedasticity of gaussian distribution, therefore, the return of the existing technology is not the best. In this paper, the characteristics of heteroscedasticity Gaussian noise are introduced into the model TSVR, and the twin support vector regression model based on heteroscedasticity Gaussian noise (TSVR-HGN) is constructed. The Lagrange multiplier method is used to solve the problem, and the optimization algorithm is used to find the global optimization. The artificial data set, UCI data set and wind speed data set were selected for experimental comparison. The experimental results show that TSVR-HGN has better prediction accuracy.INDEX TERMS Twin support vector regression machine, heteroscedastic gaussian noise, short-term wind speed forecasting, inequality constraint.

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Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model

Cited by 8 publications

References 39 publications

Time-Delay Twin Support Vector Regression- Based Adaptive Predistorter for LED Nonlinearity in Visible Light Communications

Time-Delay Twin Support Vector Regression- Based Adaptive Predistorter for LED Nonlinearity in Visible Light Communications

Vegetable price forecast based on improved WTLSSVR model

Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application

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