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Recurrent neural network for complex-variable pseudoconvex optimization with equality constraints

  • Xingnan Wen
  • , Wen Han
  • , Sitian Qin*
  • *Corresponding author for this work
  • Harbin Institute of Technology Weihai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, for solving a complex-variable pseudoconvex optimization with equality constraints, a one-layer recurrent neural network is proposed. From any initial point, the state of the presented neural network is proved to converge exponentially to the feasible region of the pseudoconvex optimization problem. When the initial point belongs to the feasible region, the state solution will converge to an optimal solution of the complex-variable pseudoconvex optimization ultimately. Compared with known neural networks for complex-variable pseudoconvex optimization, the model of the proposed neural network is less complex. Finally, numerical examples are provided to substantiate the feasibility of the proposed neural network.

Original languageEnglish
Title of host publicationProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages352-357
Number of pages6
ISBN (Electronic)9781538643624
DOIs
StatePublished - 8 Jun 2018
Externally publishedYes
Event10th International Conference on Advanced Computational Intelligence, ICACI 2018 - Xiamen, Fujian, China
Duration: 29 Mar 201831 Mar 2018

Publication series

NameProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018

Conference

Conference10th International Conference on Advanced Computational Intelligence, ICACI 2018
Country/TerritoryChina
CityXiamen, Fujian
Period29/03/1831/03/18

Keywords

  • Complex-variable pseudoconvex optimization
  • Exponential convergence
  • One-layer neural networks

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