A Two-Time-Scale Neurodynamic Approach to Constrained Minimax Optimization

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 3; pp. 620 - 629
• Main Authors: Le, Xinyi, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: H-infinity control; Learning systems; Linear programming; Mathematical model; Minimax problem; Minimax technique; Neural networks; neurodynamic optimization; Neurodynamics; Objective function; Observers; Optimization; Predictive control; Recurrent neural networks; recurrent neural networks (RNNs); two-time-scale systems
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2016.2538288

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.