A One-Layer Recurrent Neural Network for Interval-Valued Optimization Problem with Linear Constraints

摘要

In this paper, the interval-valued optimization problem is converted to a general problem in the parametric form and its solution is efficient. We present a one-layer recurrent neural network for solving this interval-valued optimization problem with linear constraints. Based on this approach, we prove that the recurrent neural network is stable in the sense of Lyapunov and the equilibrium point of the neural network is globally convergent to the optimal solution. The proposed approach improves the algorithm for the interval-valued optimization and the model is easy to implement. Finally, two numerical examples are provided to show the feasibility and effectiveness of the proposed approach.