A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints

摘要

A one-layer recurrent neural network is developed to solve pseudoconvex optimization with box constraints. Compared with the existing neural networks for solving pseudoconvex optimization, the proposed neural network has a wider domain for implementation. Based on Lyapunov stable theory, the proposed neural network is proved to be stable in the sense of Lyapunov. By applying Clarke's nonsmooth analysis technique, the finite-time state convergence to the feasible region defined by the constraint conditions is also addressed. Illustrative examples further show the correctness of the theoretical results.