An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming

Ke Chen, Zhaoxiang Zhang

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    3 Sitaatiot (Scopus)

    Abstrakti

    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.
    AlkuperäiskieliEnglanti
    OtsikkoAdvances in Brain Inspired Cognitive Systems
    Alaotsikko8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
    KustantajaSpringer International Publishing
    ISBN (elektroninen)978-3-319-49685-6
    ISBN (painettu)978-3-319-49684-9
    DOI - pysyväislinkit
    TilaJulkaistu - 2016
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaAdvances in Brain Inspired Cognitive Systems -
    Kesto: 1 tammik. 2000 → …

    Julkaisusarja

    NimiLecture Notes in Computer Science
    Vuosikerta10023
    ISSN (painettu)0302-9743

    Conference

    ConferenceAdvances in Brain Inspired Cognitive Systems
    Ajanjakso1/01/00 → …

    Julkaisufoorumi-taso

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