A New Method for Twisted Wire Crosstalk Estimation Based on GA-BP Neural Network Algorithm

Zhang, Wu; Wu, Yongjian; Ding, Jiafei; Zhao, Yao; He, Min

doi:10.13052/2022.aces.j.370601

Cited by 2 publications

(2 citation statements)

References 17 publications

(32 reference statements)

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“…Surrogates are approximate models that allow predictions and evaluations in a short time by analyzing and modelling a small amount of EM simulation data, thereby improving computational efficiency while maintaining some accuracy. Some modelling methods such as Artificial Neural Networks (ANN) [3][4][5], Support Vector Machines (SVM) [6,7], Extreme Learning Machines (ELM) [8][9][10], Gaussian Processes (GP) [11][12][13] and Backpropagation (BP) [14] are currently in use and can effectively solve electromagnetic problems.…”

Section: Introductionmentioning

confidence: 99%

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Ren,

Tian,

et al. 2024

ACES Journal

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When modelling and optimizing antennas by machine learning (ML) methods, it is the most time-consuming to obtain the training samples with labels from full-wave electromagnetic simulation software. To address the problem, this paper proposes an optimization method based on the consensus results of multiple independently trained Student’s-T Process (STP) with excellent generalization ability. First, the STP is introduced as a surrogate model to replace the traditional Gaussian Process (GP), and the hyperparameters of the STP model are optimized. Afterwards, a consistency algorithm is used to process the results of multiple independently trained STPs to improve the reliability of the results. Furthermore, an aggregation algorithm is adopted to reduce the error obtained in the consistency results if it is greater than the consistency flag. The effectiveness of the proposed model is demonstrated through experiments with rectangular microstrip antennas (RMSA) and circular microstrip antennas (CMSA). The experimental results show that the use of multiple independently trained STPs can accelerate the antenna design optimization process, and improve modelling accuracy while maintaining modelling efficiency, which has high generalization ability.

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Section: Introductionmentioning

confidence: 99%

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Ren,

Tian,

et al. 2024

ACES Journal

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“…The characteristics and advantages of Artificial Neural Networks are mainly reflected in three aspects [6][7]:…”

Section: Introductionmentioning

confidence: 99%

Research and application of improved neural network optimization algorithm

Sun¹

2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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In order to improve the computational efficiency and accuracy of the neural network algorithm, this research establishes an optimized neural network algorithm. Firstly, the optimal training function and the optimal number of hidden layer nodes of the neural network are obtained by using the empirical formula method; secondly, the prediction accuracy of the neural network is optimized, and the science of the neural network is improved. Finally, with the network as the core algorithm, the quantitative adjustment rate is used as the weight coefficient to improve the evaluation and calculation of the impact factor MIV.

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A New Method for Twisted Wire Crosstalk Estimation Based on GA-BP Neural Network Algorithm

Cited by 2 publications

References 17 publications

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Research and application of improved neural network optimization algorithm

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