Generalized recurrent neural network for ∈-insensitive support vector regression

摘要

In this paper, a generalized recurrent neural network is proposed for solving ∈-insensitive support vector regression (∈-ISVR). The ∈-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.