Further investigations on noise-tolerant zeroing neural network for time-varying quadratic programming with robotic applications

Mei Liu, Shuai Li, Yinyan Zhang, Long Jin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Recently, a modified zeroing neural network (MZNN) model has been presented for solving quadratic programming problems, which is of noise-tolerant ability. In this paper, we conduct further investigations on such a model and then present a nonlinear function activated model. Finally, the presented nonlinear function activated model is applied to the motion control of robots.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PublisherAssociation for Computing Machinery
Pages107-112
Number of pages6
ISBN (Electronic)9781450352840
DOIs
Publication statusPublished - 10 Aug 2017
Event2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of
Duration: 10 Aug 201713 Aug 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F132084

Conference

Conference2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period10/08/1713/08/17

Keywords

  • Redundant manipulators
  • Time-varying quadratic programming
  • Zeroing neural networks

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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