A continuous-time recurrent neural network for real-time support vector regression

Abstract

This paper presents a continuous-time recurrent neural network described by differential equations for realtime support vector regression (SVR). The SVR is first formulated as a convex quadratic programming problem, and then a continuous-time recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on an illustrative example are given to demonstrate the effectiveness and performance of the proposed neural network.