A one-layer discrete-time projection neural network for support vector classification

This paper presents a one-layer discrete-time projection neural network described by difference equations for real-time support vector classification (SVC). The SVC is first formulated as a convex quadratic programming problem, and then a recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on two illustrative examples are given to demonstrate the effectiveness and performance of the proposed neural network.