Generalized recurrent neural network for epsilon-insensitive support vector regression
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.
generalized recurrent neural network epsilon-insensitive support vector regression non-smooth optimization global convergence