An application of a practical neural network model for solving support vector regression problems

Article type: Research Article

Authors: Feizi, Amir^* | Nazemi, Alireza

Affiliations: Department of Mathematics, School of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran

Correspondence: [*] Corresponding author: Amir Feizi, Department of Mathematics, School of Mathematical Sciences, Shahrood University of Technology, P.O. Box 3619995161-316, Shahrood, Iran. Tel./Fax: +98 23 32300235; E-mail: [email protected].

Abstract: This paper offers a recurrent neural network to support vector machine (SVM) learning in regression arising widespread applications in a variety of setting. The SVM learning problem in regression is first converted into an equivalent quadratic programming (QP) formulation. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees to obtain the optimal solution of the support vector regression (SVR). The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. Two illustrative examples provide a further demonstration of the effectiveness of the method.

Keywords: Neural network, support vector regression, quadratic optimization, convergent, stability

DOI: 10.3233/IDA-163145

Journal: Intelligent Data Analysis, vol. 21, no. 6, pp. 1443-1461, 2017