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首页> 外文期刊>Mathematical Problems in Engineering >Hopfield Neural Network Method for Problem of Telescoping Path Optimization of Single-Cylinder Pin-Type Multisection Boom
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Hopfield Neural Network Method for Problem of Telescoping Path Optimization of Single-Cylinder Pin-Type Multisection Boom

机译：单缸销型多相臂伸缩路径优化问题的Hopfield神经网络方法

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摘要

Telescoping path optimization (TPO) of single-cylinder pin-type multisection boom (SPMB) is a practical engineering problem that is valuable to investigate. This article studies the TPO problem and finds the key of TPO is to obtain the maximum retraction backmost combination. A mathematic model on the basis of the quadratic penalty function of a Hopfield neural network (HNN) is constructed. Two strategies are presented to improve the performance of TPO model: one is proportional integral derivative (PID) strategy that adaptively adjusts the parameter lambda of the constrained term and the parameter gamma of the optimization objective term by controlling the value of constraint violation gk and the other is efficiency factor strategy that an efficiency factor is introduced in model for prioritizing the constrained term over the objective term. Data test shows that compared with the path of boom length changing before optimization, both the number of sections that need to be moved and the total travels of cylinder can be reduced by 10%-30% after optimization. Both the PID strategy and the efficiency factor strategy achieve good optimization effects. The efficiency factor strategy is excellent at moderating the conflicts between the constrained term and the objective term; thus the generations of the valid and the optimal solutions get well improved.
机译：单缸销型多极臂（SPMB）的伸缩路径优化（TPO）是一个实用的工程问题，可以对调查有价值。本文研究了TPO问题并找到了TPO的关键是为了使最大缩回最令人回归。构建了基于Hopfield神经网络（HNN）的二次惩罚功能的数学模型。提出了两种策略来提高TPO模型的性能：一种是通过控制约束违规GK的价值，自适应地调整约束期限和参数伽马参数Lamma的比例积分衍生物（PID）策略。其他是效率因子策略，即在用于优先考虑目标术语的受限术语的模型中引入了效率因子。数据测试表明，与优化前的臂长长度的路径相比，优化后，汽缸的总行程都可以减少10％-30％。 PID策略和效率因子策略都实现了良好的优化效果。效率因子策略在制度受约束期和目标术语之间的冲突方面非常出色;因此，有效和最佳解决方案的几代人得到了很好的改进。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第15期|14.1-14.14|共14页
  • 作者

    Mao Yan; Cheng Kai;

  • 作者单位

    Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Jilin Peoples R China;

    Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Jilin Peoples R China;

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