Generalized recurrent neural network for ϵ-insensitive support vector regression

Yan Zhao and Qingshan Liu

Mathematics and Computers in Simulation (MATCOM), 2012, vol. 86, issue C, 2-9

Abstract: In this paper, a generalized recurrent neural network is proposed for solving ϵ-insensitive support vector regression (ϵ-ISVR). The ϵ-ISVR is first formulated as a convex non-smooth programming problem, and then a generalize recurrent neural network with lower model complexity is designed for training the support vector machine. Furthermore, simulation results are given to demonstrate the effectiveness and performance of the proposed neural network.

Keywords: Non-smooth optimization; ϵ-Insensitive support vector regression; Generalized recurrent neural network; Global convergence (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:86:y:2012:i:c:p:2-9

DOI: 10.1016/j.matcom.2012.03.013

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