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Engineering with Computers
Erschienen in: Engineering with Computers 2/2022

07.01.2021 | Original Article

Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

verfasst von: Amir Feizi, Alireza Nazemi, Mohammad Reza Rabiei

Erschienen in: Engineering with Computers | Sonderheft 2/2022

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Abstract

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.
Vorheriger Artikel SChoA: a newly fusion of sine and cosine with chimp optimization algorithm for HLS of datapaths in digital filters and engineering applications
Nächster Artikel Automatic Delaunay mesh generation method and physically-based mesh optimization method on two-dimensional regions

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Metadaten
Titel
Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model
verfasst von
Amir Feizi
Alireza Nazemi
Mohammad Reza Rabiei
Publikationsdatum
07.01.2021
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe Sonderheft 2/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-020-01214-5

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