This paper presents a two-time-scale neurodynamic approach to constrained minimax optimization using two coupled neural networks. One of the recurrent neural networks is used for minimizing the objective function and another is used for maximization. It is shown that the coupled neurodynamic systems operating in two different time scales work well for minimax optimization. The effectiveness and characteristics of the proposed approach are illustrated using several examples. Furthermore, the proposed approach is applied for H∞ model predictive control.
|Number of pages||10|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - Mar 2017|
- Minimax problem
- neurodynamic optimization
- recurrent neural networks (RNNs)
- two-time-scale systems