An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming

Ke Chen, Zhaoxiang Zhang

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

    2 Citations (Scopus)


    Encouraged by the success of conventional GradientNet and recently-proposed ZhangNet for online equality-constrained quadratic programming problem, an improved recurrent network and its electronic implementation are firstly proposed and developed in this paper. Exploited in the primal form of quadratic programming with linear equality constraints, the proposed neural model can solve the problem effectively. Moreover, compared to the existing recurrent networks, i.e., GradientNet (GN) and ZhangNet (ZN), our model can theoretically guarantee superior global exponential convergence performance. Robustness performance of our such neural model is also analysed under a large model implementation error, with the upper bound of stead-state solution error estimated. Simulation results demonstrate theoretical analysis on the proposed model for online equality-constrained quadratic programming.
    Original languageEnglish
    Title of host publicationAdvances in Brain Inspired Cognitive Systems
    Subtitle of host publication8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
    PublisherSpringer International Publishing
    ISBN (Electronic)978-3-319-49685-6
    ISBN (Print)978-3-319-49684-9
    Publication statusPublished - 2016
    Publication typeA4 Article in conference proceedings
    EventAdvances in Brain Inspired Cognitive Systems -
    Duration: 1 Jan 2000 → …

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    ConferenceAdvances in Brain Inspired Cognitive Systems
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1


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