Title
Recurrent neural network for optimizing a continuously differentiable objective function with bound constraints
Document Type
Conference Proceeding
Publication Date
12-1-1999
Abstract
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.
Publication Source (Journal or Book title)
Proceedings of the IEEE Conference on Decision and Control
First Page
2649
Last Page
2654
Recommended Citation
Liang, X., & Wang, J. (1999). Recurrent neural network for optimizing a continuously differentiable objective function with bound constraints. Proceedings of the IEEE Conference on Decision and Control, 3, 2649-2654. Retrieved from https://repository.lsu.edu/eecs_pubs/861