STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK

Aiming at the problems that traditional BP neural network surrogate model had deficiency of fitting accuracy and computational efficiency, the Mind Evolutionary Algorithm was used to optimize BP neural network and an improved BP neural network surrogate model reliability calculation method was propo...

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Main Authors: LI YongHua, CHEN Peng, TIAN ZongRui, CHEN ZhiHao
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2021-01-01
Series:Jixie qiangdu
Subjects:
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.013
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author LI YongHua
CHEN Peng
TIAN ZongRui
CHEN ZhiHao
author_facet LI YongHua
CHEN Peng
TIAN ZongRui
CHEN ZhiHao
author_sort LI YongHua
collection DOAJ
description Aiming at the problems that traditional BP neural network surrogate model had deficiency of fitting accuracy and computational efficiency, the Mind Evolutionary Algorithm was used to optimize BP neural network and an improved BP neural network surrogate model reliability calculation method was proposed. Firstly, the Mind Evolutionary Algorithm was used to optimize the weights and thresholds of BP neural network to obtain the optimal initial value. Secondly, the Bayesian Regularization algorithm was used to train the optimized neural network to establish MEA-BR-BP neural network surrogate model and verify the effectiveness of the improved surrogate model used test function. Finally, the reliability calculation results were calculated with the Monte Carlo method. The results show that the proposed method improves the fitting accuracy and gives consideration to the calculation efficiency, which verifies the superiority and feasibility of the proposed method.
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spelling doaj.art-3e4459a8411a4402a747f8dd6528809c2023-08-01T07:54:22ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-01431359136530612514STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORKLI YongHuaCHEN PengTIAN ZongRuiCHEN ZhiHaoAiming at the problems that traditional BP neural network surrogate model had deficiency of fitting accuracy and computational efficiency, the Mind Evolutionary Algorithm was used to optimize BP neural network and an improved BP neural network surrogate model reliability calculation method was proposed. Firstly, the Mind Evolutionary Algorithm was used to optimize the weights and thresholds of BP neural network to obtain the optimal initial value. Secondly, the Bayesian Regularization algorithm was used to train the optimized neural network to establish MEA-BR-BP neural network surrogate model and verify the effectiveness of the improved surrogate model used test function. Finally, the reliability calculation results were calculated with the Monte Carlo method. The results show that the proposed method improves the fitting accuracy and gives consideration to the calculation efficiency, which verifies the superiority and feasibility of the proposed method.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.013BP neural network;Surrogate model;Mind evolutionary algorithm;Reliability analysis;Bogie frame
spellingShingle LI YongHua
CHEN Peng
TIAN ZongRui
CHEN ZhiHao
STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
Jixie qiangdu
BP neural network;Surrogate model;Mind evolutionary algorithm;Reliability analysis;Bogie frame
title STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
title_full STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
title_fullStr STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
title_full_unstemmed STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
title_short STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
title_sort structure reliability calculation method based on improved neural network
topic BP neural network;Surrogate model;Mind evolutionary algorithm;Reliability analysis;Bogie frame
url http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.013
work_keys_str_mv AT liyonghua structurereliabilitycalculationmethodbasedonimprovedneuralnetwork
AT chenpeng structurereliabilitycalculationmethodbasedonimprovedneuralnetwork
AT tianzongrui structurereliabilitycalculationmethodbasedonimprovedneuralnetwork
AT chenzhihao structurereliabilitycalculationmethodbasedonimprovedneuralnetwork