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首页> 外文期刊>Mathematical Problems in Engineering >Neural Network for Solving SOCQP and SOCCVI Based on Two Discrete-Type Classes of SOC Complementarity Functions
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Neural Network for Solving SOCQP and SOCCVI Based on Two Discrete-Type Classes of SOC Complementarity Functions

机译：基于两个离散类型的SOC互补函数的神经网络解决SOCQP和SOCCVI

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摘要

This paper focuses on solving the quadratic programming problems with second-order cone constraints (SOCQP) and the second-order cone constrained variational inequality (SOCCVI) by using the neural network. More specifically, a neural network model based on two discrete-type families of SOC complementarity functions associated with second-order cone is proposed to deal with the Karush-Kuhn-Tucker (KKT) conditions of SOCQP and SOCCVI. The two discrete-type SOC complementarity functions are newly explored. The neural network uses the two discrete-type families of SOC complementarity functions to achieve two unconstrained minimizations which are the merit functions of the Karuch-Kuhn-Tucker equations for SOCQP and SOCCVI. We show that the merit functions for SOCQP and SOCCVI are Lyapunov functions and this neural network is asymptotically stable. The main contribution of this paper lies on its simulation part because we observe a different numerical performance from the existing one. In other words, for our two target problems, more effective SOC complementarity functions, which work well along with the proposed neural network, are discovered.
机译：本文侧重于解决二阶锥限制（SOCQP）的二次编程问题，并通过使用神经网络来限制变分不等式（SOCCVI）。更具体地，提出了一种基于与二阶锥相关的SOC互补函数的两个离散类型系列的神经网络模型，以处理SoCQP和SoCCVI的Karush-Kuhn-Tucker（KKT）条件。新探索了两个离散类型的SOC互补功能。神经网络使用SoC互补功能的两个离散类型的族，以实现两个无约束的最小化，这是SoCQP和SoCCVI的Karuch-Kuhn-Tucker方程的优势函数。我们表明SoCQP和SOCCVI的优点函数是Lyapunov功能，而这种神经网络是渐近的稳定性。本文的主要贡献在于其仿真部分，因为我们观察了现有的不同数值。换句话说，对于我们的两个目标问题，发现了更有效的SoC互补功能，以及所提出的神经网络的运作良好。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第4期|4545064.1-4545064.18|共18页
  • 作者

    Sun Juhe; Wu Xiao-Ren; Saheya B.; Chen Jein-Shan; Ko Chun-Hsu;

  • 作者单位

    Shenyang Aerosp Univ Sch Sci Shenyang 110136 Liaoning Peoples R China;

    Natl Taiwan Normal Univ Dept Math Taipei 11677 Taiwan;

    Inner Mongolia Normal Univ Coll Math Sci Hohhot 010022 Inner Mongolia Peoples R China;

    Natl Taiwan Normal Univ Dept Math Taipei 11677 Taiwan;

    I Shou Univ Dept Elect Engn Kaohsiung 840 Taiwan;

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