A recurrent neural network for optimizing a continuously differentiable objective function with bound constraints

This paper presents a continuous-time recurrent neural network model for optimizing any continuously differentiable objective function subject to bound constraints. The proposed recurrent neural network has several desirable properties such as regularity and global exponential stability. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.