Improved SVR based on CARS and BAS for hydrocarbon concentration detection

Liang, Haibo; Liu, Gang; Li, Zhongbing; Ren, Wei

doi:10.1016/j.vibspec.2023.103494

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…In the future, we should further enhance the emergency supply capacity of vegetables, enhance the self-sufficiency level of mega cities and first tier cities, reduce circulation links and related costs, improve quality and optimize the reserve structure, and ensure effective supply of vegetables and stable prices. Many researchers have proposed regression prediction models, such as the support vector machine (SVM) [1] has been effectively utilized for feature selection [2], density estimation [3], and function approximation [4] due to its strong generalization performance, which is supported by statistical learning theory and Vapnik-Chervonenkis dimension theory.SVM stands out from other machine learning techniques, like artificial neural networks [5], due to its distinct benefits. Firstly, SVM offers a singular global optimal solution that eliminates the possibility of encountering a local optimal solution when tackling QPP.…”

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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