Convergence of the Projection-Based Generalized Neural Network and the Application to Nonsmooth Optimization Problems

Abstract

This paper introduces a projection-based generalized neural network, which can be used to solve a class of nonsmooth convex optimization problems It generalizes the existing projection neural networks for solving the optimization problems In addition, the existence and convergence of the solution for the generalized neural networks are proved Moreover, we discuss the application to nonsmooth convex optimization problems And two illustrative examples are given to show the efficiency of the theoretical results.