Crosstalk Prediction in Twisted-Wire Pairs Based on Beetle Swarm Optimization Algorithm

Abstract

In this paper, a new algorithm for predicting per unit length (p.u.l) parasitic parameters of transmission line is proposed. In the twisted-wire pair (TWP), different rotation degrees correspond to different parasitic parameters, which brings difficulties to the solution of telegraph equation. At present, the mainstream method is to divide TWP and use the cascade theory to solve each segment as a parallel transmission line. Therefore, we propose to use the beetle swarm optimization (BSO) algorithm to optimize the weights of the back propagation neural network, use a certain sample to train the network. This algorithm is used to predict the p.u.l parameters of uniform and non-uniform TWP, and the finite-difference time-domain (FDTD) method is used to solve telegraph equation. Among them, the crosstalk value of the uniform TWP is compared with the simulation value in the CST to verify the effectiveness of the proposed algorithm; the non-uniform TWP performs 500 random generation calculations to give the maximum envelope value of the crosstalk.INDEX TERMS Beetle swarm optimization (BSO), crosstalk prediction, finite-difference time-domain (FDTD), multiconductor transmission lines (MTL).

Multi‐conductor electromagnetic coupling model for sensitive wires subject to driven wire in complex electronic systems

“…So, more efficient methods must be proposed to increase design efficiency. [23][24][25][26] A method for predicting random cable bundle crosstalk is proposed, 23 which considers the random rotation of the cross-section and introduces a back propagation neural network algorithm (BAS-BPNN) optimized by the beetle antenna search method. Numerical experimental results show that the proposed method has higher accuracy and a broader frequency range.…”

“…A new nonuniform pitch twisted‐pair crosstalk estimation method is proposed based on the study of the Monte Carlo (MC) method and the adaptive beetle antennae search (ABAS) algorithm 25 . A new algorithm for predicting parasitic parameters per unit length of transmission lines is proposed, 26 where the beetle swarm optimization (BSO) algorithm is used to optimize the weight of the back‐propagation neural network. The general idea of these works is to train high‐performance surrogate models to evaluate the effects of some uncertain parameters of wires.…”

“…It still takes a few minutes to complete the full electromagnetic simulation, which cannot provide instantaneous or near‐instantaneous responses. So, more efficient methods must be proposed to increase design efficiency 23–26 . A method for predicting random cable bundle crosstalk is proposed, 23 which considers the random rotation of the cross‐section and introduces a back propagation neural network algorithm (BAS‐BPNN) optimized by the beetle antenna search method.…”

Crosstalk analysis of twisted bundle of twisted wire pairs (TBTWPs) and star‐quad cables under the effect of inner and outer excitations

“…In Reference 31, a BP‐based NN combined with the finite‐difference time‐domain (FDTD) method is employed to predict three‐core TWP crosstalk. Additionally, in order to improve the nonlinear mapping ability of the BP neural network, a neural network algorithm based on backpropagation optimized by the beetle antennae search method (BAS‐BPNN) is proposed in References 32–35. It is worth noting that the excitation sources used in these studies are only single‐ended sources 31–33 .…”