李琳娜,李甜,钟冬望,涂圣武,刘洋

• 1:武汉科技大学理学院
• 2:武汉科技大学冶金工业过程系统科学湖北省重点实验室
• 3:武汉电力职业技术学院电力工程系

摘要(Abstract)：

为了保证水下爆炸容器在服役期间的安全性,有必要进行容器的动态响应预测。对服役期的水下爆炸容器在不同载荷条件下进行动态响应测试试验,选取时间、药量、加载静水压和应变片位置的11个亚变量,共14个影响因素作为输入变量,容器的最大应力作为输出变量,建立基于GRNN的水下爆炸容器动态响应预测模型,仿真载荷与容器应变的映射关系,并通过10折交叉验证法验证了该模型具有较好的预测性能。同时对比基于BPNN的预测模型,GRNN模型的拟合与预测性能明显优于BPNN模型,进一步说明了GRNN方法在水下爆炸容器动态响应预测过程中的有效性。

关键词(KeyWords)： 水下爆炸容器;动态响应;广义回归神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(51404175、51574184);; 冶金工业过程系统科学湖北省重点实验室开放基金资助项目(Y201712);; 武汉科技大学国防预研基金资助项目(GF201708)

作者(Author): 李琳娜,李甜,钟冬望,涂圣武,刘洋

参考文献(References)：

• [1]钟冬望,李琳娜.水介质爆炸容器动力响应分析与优化设计[M].北京:科学出版社,2016:134-147.
• [2]徐立鹏. FFT-BP神经网络模型对车载γ能谱辐射剂量率的预测分析[J].光谱学与光谱分析,2018,38(2):590-594.XU Li-peng. Research of carborneγ-Ray energe spectrum radiation dose rate based on FFT-BP network model[J].Spectroscopy and Spectral Analysis,2018,38(2):590-594.(in Chinese)
• [3]张亮,赵娜.基于BP神经网络-SOSM的结构可靠性分析[J].自动化技术与应用,2016,35(3):14-19.ZHANG Liang,ZHAO Na. Structural reliability analysis based on BP Neural Network-SOSM[J]. Automation Technology and Applications,2016,35(3):14-19.(in Chinese)
• [4]姜春雷.基于SARIMA-BP神经网络组合方法的MODIS叶面积指数时间序列建模与预测[J].光谱学与光谱分析,2017,37(1):189-193.JIANG Chun-lei. Modeling and predicting of MODIS leaf area index time series based on a hybrid SARIMA and BP neural network method[J]. Spectroscopy and Spectral Analysis,2017,37(1):189-193.(in Chinese)
• [5]杜剑.基于卷积神经网络与光谱特征的夏威夷果品质鉴定研究[J].光谱学与光谱分析,2018,38(5):1514-1519.DU Jian. Study on quality identification of macadamia nut based on convolutional neural networks and spectral features[J]. Spectroscopy and Spectral Analysis,2018,38(5):1514-1519.(in Chinese)
• [6]王璨.卷积神经网络用于近红外光谱预测土壤含水率[J].光谱学与光谱分析,2018,38(1):36-41.WANG Can. Convolutional neural network application in prediction of soil moisture content[J]. Spectroscopy and Spectral Analysis,2018,38(1):36-41.(in Chinese)
• [7]李越胜.基于BP神经网络和激光诱导击穿光谱的燃煤热值快速测量方法研究[J].光谱学与光谱分析,2017,37(8):2575-2579.LI Yue-sheng. Detection of caloric value of coal using laser-induced breakdown spectroscopy combined with BP Neural Networks[J]. Spectroscopy and Spectral Analysis,2017,37(8):2575-2579.(in Chinese)
• [8]杨榛.基于神经网络的炭气凝胶孔结构的预测与优化模型研究[J].新型炭材料,2017,32(1):77-85.YANG Zhen. Modelling and optimization of the pore structure of carbon aerogels using an artificial neural network[J]. New Carbon Materials,2017,32(1):77-85.(in Chinese)
• [9]柯文豪.基于GRNN神经网络的沥青路面裂缝预测方法[J].深圳大学学报理工版,2017,34(4):378-384.KE Wen-hao. Prediction method for asphalt pavement crack based on GRNN neural network[J]. Journal of Shenzhen University Science and Engineering,2017,34(4):378-384.(in Chinese)
• [10]张燕君.基于自适应变异果蝇优化算法和广义回归神经网络的布里渊散射谱特征提取[J].光谱学与光谱分析,2015,35(10):2916-2922.ZHANG Yan-jun. A brillouin scattering spectrum feature extraction based on files optimization algorithm with adaptive mutation and generalized regression neural network[J]. Spectroscopy and Spectral Analysis,2015,35(10):2916-2922.(in Chinese)
• [11] FANG Wen-hui,LU Wei,XU Hong-li,et al. Study on the detection of rice seed germination rate based on infrared thermal imaging technology combined with generalized regression neural network[J]. Spectroscopy and Spectral Analysis,2016,36(8):2692-2697.
• [12]曹岩枫,徐诚.基于果蝇算法优化广义回归神经网络的机枪枪管初速衰减建模与预测[J].兵工学报,2017,38(1):1-8.CAO Yan-feng,XU Cheng. Modeling and prediction of muzzle velocity degradation of machine gun based on FOAGRNN[J]. Acta Armamentarii,2017,38(1):1-8.(in Chinese)
• [13]李琳娜.水介质爆炸容器动力响应分析与实验研究[D].武汉:武汉科技大学,2013:72-86.LI Lin-na. Dynamic responses analysis and experimental study on the water medium explosion vessels[D]. Wuhan:Wuhan University of Science and Technology,2013:72-86.(in Chinese)
• [14]张德丰. MATLAB神经网络应用设计[M].北京:机械工业出版社,2009:181-183.