Generalized recurrent neural network for ϵ-insensitive support vector regression

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.

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Year of publication:	2012
Authors:	Zhao, Yan ; Liu, Qingshan
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 86.2012, C, p. 2-9
Publisher:	Elsevier
Subject:	Non-smooth optimization \| ϵ-Insensitive support vector regression \| Generalized recurrent neural network \| Global convergence

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011051206