Journal
ENGINEERING WITH COMPUTERS
Volume 38, Issue SUPPL 2, Pages 1005-1020Publisher
SPRINGER
DOI: 10.1007/s00366-020-01214-5
Keywords
Neural network; Support vector regression; Quadratic optimization; Convergent; Stability; Probabilistic constraints
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This paper introduces a recurrent neural network to assist support vector machine learning in stochastic support vector regression, demonstrating its effectiveness in three illustrative examples.
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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