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Engineering with Computers
Published 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

Authors: Amir Feizi, Alireza Nazemi, Mohammad Reza Rabiei

Published in: Engineering with Computers | Special Issue 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.
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Metadata
Title
Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model
Authors
Amir Feizi
Alireza Nazemi
Mohammad Reza Rabiei
Publication date
07-01-2021
Publisher
Springer London
Published in
Engineering with Computers / Issue Special Issue 2/2022
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-020-01214-5

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