A rapid method to predict biaxial fatigue life of automotive wheels using proper orthogonal decomposition and radial basis function algorithm

Luo, Jintao; Shan, Yingchun; Liu, Xiandong; Zhang, Yue; Jiang, Er; Kong, Decai

doi:10.1016/j.advengsoft.2023.103543

Cited by 3 publications

(2 citation statements)

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“…wherein the basis function ϕ is a nonlinear function, and X p is ϕ the center of the data, and the independent variable is the distance X − Xp between the data X and the center X p . F(X) based on radial basis functions is defined as a linear combination of radial basis functions, as shown in Formula (14).…”

Section: Model Constructionmentioning

confidence: 99%

“…Lashkenari et al [13] developed a radial basis function neural network prediction model for predicting the viscosity of Iranian crude oil that has universality and accuracy. Luo et al [14] proposed a fast method for predicting the fatigue life of automotive wheels based on radial basis function neural networks combined with orthogonal decomposition, which has ideal accuracy. Mohammad et al [15] established a concrete mix ratio model based on radial basis functions to estimate the compressive strength of concrete containing different amounts of fly ash at Electronics 2023, 12, 4677 2 of 14 any time in response to the fact that fly ash can enhance the mechanical properties and durability of concrete materials.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Yu,

Zheng,

Zhao

2023

Electronics

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A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data characteristics and distribution differences on the prediction model. Finally, based on different prediction objectives, time-driven and data-driven calibration data prediction models are constructed using RBF neural networks. Through simulation analysis, it is shown that an RBF neural network is superior to a BP neural network in predicting clock tester calibration data, and time-driven prediction accuracy is superior to data-driven prediction accuracy. Moreover, the prediction error and mean square error of both prediction models are on the order of 10−9, meeting the prediction accuracy requirements.

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Section: Model Constructionmentioning

confidence: 99%