A Recurrent Neural Network for Non-smooth Convex Programming Subject to Linear Equality and Bound Constraints

摘要

In this paper, a recurrent neural network model is proposed for solving non-smooth convex programming problems, which is a natural extension of the previous neural networks. By using the non-smooth analysis and the theory of differential inclusions, the global convergence of the equilibrium is analyzed and proved. One simulation example shows the convergence of the presented neural network.