Distribution-Allowed Noise-Resistant Neural Dynamics for Constrained Time-Dependent Quadratic Programming With kWTA Application

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xin Ma;Dexiu Ma;Mei Liu
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引用次数: 0

Abstract

Existing computational models for addressing time-dependent quadratic programming (TDQP) problems encounter some challenges, such as generating lagging errors, lack of noise immunity, and inability to be distributed. To handle these challenges, this article proposes a distribution-allowed noise-resistant neural dynamics (DANRND) model to solve TDQP problems with equality and inequality constraints by introducing auxiliary variables rather than by using the nonlinear complementary problem (NCP) function. The proposed model is able to effectively eliminate the hysteresis error and suppress the influence of noises. Specifically, the proposed model is capable of implementation in a distributed manner, which extends its scope of applications. Then, theoretical analyses are provided to prove the global convergence in both noise-free and noisy conditions. Simulative examples and comparison results with existing methods are offered, demonstrating the superiority of the proposed DANRND model. Finally, a distributed cooperative task based on the k-winner-take-all (kWTA) operation is performed on a multirobot platform to further verify the distributed implementation of the proposed DANRND model.
基于kWTA的约束时间相关二次规划中允许分布的抗噪声神经动力学
现有的求解时间相关二次规划(TDQP)问题的计算模型遇到了一些挑战,如产生滞后误差、缺乏抗噪声能力和不能分布。为了应对这些挑战,本文提出了一种允许分布的抗噪声神经动力学(DANRND)模型,通过引入辅助变量而不是使用非线性互补问题(NCP)函数来解决具有相等和不等式约束的TDQP问题。该模型能够有效地消除迟滞误差,抑制噪声的影响。具体来说,所提出的模型能够以分布式方式实现,从而扩展了其应用范围。然后,通过理论分析证明了该方法在无噪声和有噪声条件下的全局收敛性。最后给出了仿真算例,并与现有方法进行了比较,验证了该模型的优越性。最后,在多机器人平台上进行了基于k-赢者通吃(kWTA)操作的分布式协作任务,进一步验证了所提出的DANRND模型的分布式实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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