Two-Timescale Multilayer Recurrent Neural Networks for Nonlinear Programming

Jiasen Wang, Jun Wang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This article presents a neurodynamic approach to nonlinear programming. Motivated by the idea of sequential quadratic programming, a class of two-timescale multilayer recurrent neural networks is presented with neuronal dynamics in their output layer operating at a bigger timescale than in their hidden layers. In the two-timescale multilayer recurrent neural networks, the transient states in the hidden layer(s) undergo faster dynamics than those in the output layer. Sufficient conditions are derived on the convergence of the two-timescale multilayer recurrent neural networks to local optima of nonlinear programming problems. Simulation results of collaborative neurodynamic optimization based on the two-timescale neurodynamic approach on global optimization problems with nonconvex objective functions or constraints are discussed to substantiate the efficacy of the two-timescale neurodynamic approach.

Original languageEnglish
Pages (from-to)37-47
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume33
Issue number1
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Keywords

  • Neurodynamic optimization
  • nonlinear programming
  • two-timescale system

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