Research on Crosstalk Prediction of Stranded Cable Based on GA-BP Neural Network Algorithm

Abstract

Based on the research of genetic algorithm (GA) to optimize the Back propagation (BP) neural network algorithm, this paper proposes a method for predicting stranded wire crosstalk based on the algorithm. First, the equivalent circuit model of multi-conductor transmission line is established. Combined with the idea of modulus decoupling, the multi-conductor transmission line equation is solved. Then the mathematical model of the stranded wire is established and its structural characteristics are analyzed, and the GA-BP neural network algorithm is used to realize the mapping of the electromagnetic parameter matrix of the stranded wire and the position of the stranded wire. Finally, the mapping relationship is substituted into the transmission line equation, and the near-end crosstalk (NEXT) and the far-end crosstalk (FEXT) of an example three-core twisted wire are predicted based on the idea of cascade combined with the modulus decoupling method. By comparing with the transmission line matrix method (TLM), it can be seen that the method proposed in this paper is in good agreement with the crosstalk results obtained by the electromagnetic field numerical method, which verifies the effectiveness of the GA-BP neural network algorithm combined with modulus decoupling to predict the crosstalk of twisted wires.