Generalized recurrent neural network for ε-insensitive support vector regression

被引：5
|
作者：
Zhao, Yan [1 ]
Liu, Qingshan [2 ]
机构：
[1] Wannan Med Coll, Dept Basic Courses, Wuhu 241000, Peoples R China
[2] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2012年 / 86卷
基金：
中国国家自然科学基金;
关键词：
Non-smooth optimization; epsilon-Insensitive support vector regression; Generalized recurrent neural network; Global convergence; LIMITING ACTIVATION FUNCTION; OPTIMIZATION; MACHINE;
D O I：
10.1016/j.matcom.2012.03.013
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, a generalized recurrent neural network is proposed for solving epsilon-insensitive support vector regression (epsilon-ISVR). The epsilon-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. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：2 / 9
页数：8
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