基于GRNN建立开孔型多孔玻璃吸声性能模型
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摘要
采用广义回归神经网络(GRNN)方法,在开孔型多孔玻璃16组实验数据基础上,以12组随机数据作为训练样本,4组作为检验样本,建立以多孔玻璃厚度和孔隙率的GRNN模型,得到训练的最佳光滑因子σ=0.1,最大迭代次数为20;结果表明,模型预测值与实验值的平均误差为0.003,建立的模型精度高,预测吸声系数曲线形貌相似度高;该方法有简单、训练样本少、快速、准确等优点。
The generalized regression neural network( GRNN) model of sound absorption coefficient of porous glass was built on data from 16 groups gained by experiments,where 12 groups were randomly selected as trained samples and the other 4 groups were as tested ones. This GRNN model which has two parameters,porosity and thickness as the inputs,was set the maximum iteration number 20,getting the optimal trained spread parameter σ = 0.1.The results showed that the average error of this model was 0.003,and this model has high precision and the prediction curve of the sound absorption coefficient was very similar to the experiments. The advantages of this method are simple,needing less trained samples,rapid and accurate.
The generalized regression neural network( GRNN) model of sound absorption coefficient of porous glass was built on data from 16 groups gained by experiments,where 12 groups were randomly selected as trained samples and the other 4 groups were as tested ones. This GRNN model which has two parameters,porosity and thickness as the inputs,was set the maximum iteration number 20,getting the optimal trained spread parameter σ = 0.1.The results showed that the average error of this model was 0.003,and this model has high precision and the prediction curve of the sound absorption coefficient was very similar to the experiments. The advantages of this method are simple,needing less trained samples,rapid and accurate.
引文
[1]丁宇翔,徐颖,徐宁.连续铜纤维多孔材料吸声性能的研究[J].西北工业大学学报,2012,30(4):553-556DING Yuxiang,XU Ying,XU Ning. Experimental Study on Sound Absorption Performance of Porous Copper-Fibrous Material[J]. Journal of Northwestern Polytechnical University,2012,30(4):553-556(in Chinese)
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[9]郭斌,孟令启,杜勇,等.基于GRNN神经网络的中厚板轧机厚度预测[J].中南大学学报,2011,42(4):960-965GUO Bin,MENG Lingqi,DU Yong,et al. Thickness Prediction of Medium Plate Mill Based on GRNN Neural Network[J].Journal of Central South University,2011,42(4):960-965(in Chinese)
[10]高凌琴.基于GRNN的汽车保有量预测模型[J].山东理工大学学报,2011,25(4):85-87GAO Linqin. The forecasting Model of Vehicle Ownership Based on GRNN[J]. Journal of Shandong Unirersity of Technology,2011,25(4):85-87(in Chinese)
[11]冯志鹏,宋希庚,薛冬新,等.基于广义回归神经网络的时间序列预测研究[J].振动、测试与诊断,2003(2):29-33FENG Zhipeng,SONG Xigeng,XUE Dongxin,et al. General Regression Neural Network Based Prediction of Time Series[J].Journal of Vibration Measurement&Diagnosis,2003(2):29-33(in Chinese)
[12] LIU J,BAO W,SHI L,et al. General Regression Neural Network for Prediction of Sound Absorption Coefficients of Sandwich Structure Nonwoven Absorbers[J]. Applied Acoustics,2014,76(1):128-137
[13]刘鹏辉,杨宜谦,姚京川.多孔吸声材料的吸声特性研究[J].噪声与振动控制,2011,31(2):123-126LIU Penghui,YANG Yiqian,YAO Jingchuan. Study on Absorption Property of Porous Sound-Absorbing Materials[J]. Noise and Vibration Control,2011,31(2):123-126(in Chinese)
[14]刘新金,刘建立,徐伯俊,等.分层多孔材料吸声结构的性能分析[J].振动与冲击,2012,31(5):106-110LIU Xinjin,LIU Jianli,XU Bojun,et al. Acoustic Analysis for a Sound-Absorbing Structure with Multi-Layered Porous Material[J]. Journal of Vibration and Shock,2012,31(5):106-110(in Chinese)
[15]史峰,王小川. MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:73-80SHI Feng,WANG Xiaochuan. Analysis of 30 Cases of MATLAB Neural Network[M]. Beijing,Beihang University Press,2010:73-80(in Chinese)
[2]马大猷.现代声学理论基础[M].北京:科学出版社,2004:230-240MA Dayou. Modern acoustics theory[M]. Bei Jing,Science Press,2004:230-240(in Chinese)
[3]徐颖,李珊,王常力,等.不锈钢纤维多孔材料吸声性能的研究[J].西北工业大学学报,2015,33(3):401-404XU Ying,LI Shan,WANG Changli,et al. Exploring Sound Absorption Performance of Stainless Steel Fiber Porous Materials[J]. Journal of Northwestern Polytechnical University,2015,33(3):401-404(in Chinese)
[4]孙莉莉,祁元春.泡沫玻璃的研究进展[J].化学工程与装备,2012(1):111-113SUN Lili,QI Yuanchun. Progress and Study of Foam Glass[J]. Chemical Engineering&Equipment,2012(1):111-113(in Chinese)
[5]邹伟仁,徐颖,方庆川,等.一种新型多孔玻璃的制备及吸声性能研究[J].西北工业大学学报,2014,32(3):368-372ZOU Weiren,XU Ying,FANG Qingchuan,et al. Preparation and Absorbing Properties of a Novel Porous Glass[J]. Journal of Northwestern Polytechnical University,2014,32(3):368-372(in Chinese)
[6] DELANY M E,BAZLEY E N. Acoustical Properties of Fibrous Absorbent Materials[J]. Applied Acoustics,1970,3(7):105-116
[7] ALLARD J F,ATALA N. Propagation of Sound in Porous Media Modeling Sound Absorbing Materials[M]. New York,Elsevier Applied Science,1993:20-85
[8] TARNOW V. Airflow Resistivity of Models of Fibrous Acoustic Materials[J]. Journal of the Acoustical Society of America,1996,100(100):3706-3713
[9]郭斌,孟令启,杜勇,等.基于GRNN神经网络的中厚板轧机厚度预测[J].中南大学学报,2011,42(4):960-965GUO Bin,MENG Lingqi,DU Yong,et al. Thickness Prediction of Medium Plate Mill Based on GRNN Neural Network[J].Journal of Central South University,2011,42(4):960-965(in Chinese)
[10]高凌琴.基于GRNN的汽车保有量预测模型[J].山东理工大学学报,2011,25(4):85-87GAO Linqin. The forecasting Model of Vehicle Ownership Based on GRNN[J]. Journal of Shandong Unirersity of Technology,2011,25(4):85-87(in Chinese)
[11]冯志鹏,宋希庚,薛冬新,等.基于广义回归神经网络的时间序列预测研究[J].振动、测试与诊断,2003(2):29-33FENG Zhipeng,SONG Xigeng,XUE Dongxin,et al. General Regression Neural Network Based Prediction of Time Series[J].Journal of Vibration Measurement&Diagnosis,2003(2):29-33(in Chinese)
[12] LIU J,BAO W,SHI L,et al. General Regression Neural Network for Prediction of Sound Absorption Coefficients of Sandwich Structure Nonwoven Absorbers[J]. Applied Acoustics,2014,76(1):128-137
[13]刘鹏辉,杨宜谦,姚京川.多孔吸声材料的吸声特性研究[J].噪声与振动控制,2011,31(2):123-126LIU Penghui,YANG Yiqian,YAO Jingchuan. Study on Absorption Property of Porous Sound-Absorbing Materials[J]. Noise and Vibration Control,2011,31(2):123-126(in Chinese)
[14]刘新金,刘建立,徐伯俊,等.分层多孔材料吸声结构的性能分析[J].振动与冲击,2012,31(5):106-110LIU Xinjin,LIU Jianli,XU Bojun,et al. Acoustic Analysis for a Sound-Absorbing Structure with Multi-Layered Porous Material[J]. Journal of Vibration and Shock,2012,31(5):106-110(in Chinese)
[15]史峰,王小川. MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:73-80SHI Feng,WANG Xiaochuan. Analysis of 30 Cases of MATLAB Neural Network[M]. Beijing,Beihang University Press,2010:73-80(in Chinese)
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