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

Xinyi Le, Jun Wang

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number7460136
Pages (from-to)620-629
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume28
Issue number3
DOIs
Publication statusPublished - Mar 2017
Externally publishedYes

Keywords

  • Minimax problem
  • neurodynamic optimization
  • recurrent neural networks (RNNs)
  • two-time-scale systems

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