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首页> 外文期刊>Applied Computational Electromagnetics Society journal >Prediction and Suppression of Twisted-wire Pair Crosstalk Based on Beetle Swarm Optimization Algorithm
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Prediction and Suppression of Twisted-wire Pair Crosstalk Based on Beetle Swarm Optimization Algorithm

机译：基于甲虫群优化算法的双绞线对串扰预测与抑制

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摘要

Based on the theory of multi-conductor transmission lines (MTL), this paper proposes a new method for predicting and suppressing crosstalk of twisted-wire pair (TWP). The per unit length (p.u.l) RLCG parameters change caused by the inconsistent cross-sectional shape of TWP, changes in parameters make it difficult to solve the telegraph equation. In this paper, the method of transmission lines cascade is used. TWP is divided into several segments, and p.u.l parameters of each segment are predicted. Compared with before method, we propose a higher precision algorithm-beetle swarm optimization (BSO) to optimize the weights of back-propagation (BP) neural network, which predict p.u.l parameters at each segment. On this basis, it is divided into two steps: 1) Use MTL frequency domain method combined with lines' terminal conditions to solve crosstalk and compare with CST simulation results; 2) Use the singular value decomposition (SVD) method to add matrix modules at both ends of lines for suppressing crosstalk. The results show that proposed method in this paper is consistent with the simulation, and the accuracy is higher than before.
机译：基于多导体传输线（MTL）的理论，本文提出了一种预测和抑制扭曲线对（TWP）串扰的新方法。每单位长度（P.U.L）RLCG参数由TWP不一致的横截面形状引起的变化，参数的变化使得难以解决电报方程。在本文中，使用传输线级联的方法。 TWP分为几个段，并且预测每个段的P.U.L参数。与在方法之前相比，我们提出了更高的精确度算法 - 甲壳群优化（BSO），以优化后传播（BP）神经网络的权重，其预测每个段的P.u.l参数。在此基础上，它分为两个步骤：1）使用MTL频域方法结合线路的终端条件来解决串扰并与CST仿真结果进行比较; 2）使用奇异值分解（SVD）方法在线的两端添加矩阵模块以抑制串扰。结果表明，本文提出的方法与模拟一致，精度高于以前。

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