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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model
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Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

机译：基于闭环粒子群反馈模型的计算机网络动态平衡流分布

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摘要

Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.
机译：基于闭环粒子群反馈模型，提出一种图形化分析计算机网络动平衡系统稳定性的方法。首先，基于拥塞控制的二阶时滞系统模型，用特征伪多项式描述系统的稳定性;其次，基于反转线，通过图形分析方法验证系统的稳定性，得到保证系统稳定性的PID控制器参数范围，分析控制器比例增益边界与网络特性参数的关系;然后，在分析基本粒子群优化算法的基础上，将粒子群演化公式分为自身因素和社会因素两部分，分析了各部分对粒子群演化速度和位置的影响，提出了一种改进的粒子群。最后，根据上述分析，依次从合适的解中求出相应的方程，从而设计出一类中间变量较少的粒子群优化算法。针对经典PID控制参数整定方法涉及的系统问题，将改进的粒子群算法应用于PID控制器的参数整定和优化。实验中，将改进的PSO-PID控制器优化算法应用于主动队列管理的随机早期检测算法，研究设计了改进算法的流程，并通过仿真实验验证了改进算法的相关性能。

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