欢迎登录材料期刊网

材料期刊网

高级检索

简要介绍了基于BP反向误差传播算法和自组织特征映射算法的神经网络模型的基本原理和在材料领域中的应用.

参考文献

[1] 杨立斌,张辉,彭大暑,段炼.7075铝合金高温流变行为的研究[J].热加工工艺,2002(01):1-2,5.
[2] Adya M;Collopy F .How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation[J].Journal of Forecasting,1998(17):481.
[3] 付明玉,夏国清,陈德娟.神经网络自学习模糊控制器在直流蒸汽发生器控制中的应用[J].船舶工程,2002(03):43-47.
[4] Karlik Bekir .A neural network image recognition for control of manufacturing plant[J].Mathematical and computational applications,2003,8(08):181.
[5] Dzwinel W;Pepyolyshev Yu N et al.Predicting of slow noise and vibration spectra degradation in the IBR-2 pulsed neutron source using a neural network simulator[J].Progress in Nuclear Energy,2003,43(1-4):145.
[6] Musa Muhammad;Pant Prahlad D.Neural network models for predicting stop and stopped delay for individual vehicles at signalized intersections[M].Proceedings of the conference on traffic and transportation studies,ICTTS,1998:468.
[7] Cardon H;van Hoogstraten R;Davies P.Neural network application in geology.Identification of genetic facies[M].Proceeding of the international conference on Artificial Neural Networks,1991:1.
[8] 陈蔚岗,张志谦,陈辉,陈二龙,张国腾.人工神经网络在GMT材料设计中的应用研究[J].玻璃钢/复合材料,2002(02):5-6,23.
[9] Kuang-Hua Fuh;Shuh-Bin Wang .Force modeling and forecasting in creep feed grinding using improved BP neural network[J].International Journal of Machine Tools & Manufacture: Design, research and application,1997(8):1167-1178.
[10] 井玉安,胡林,张玉华.人工神经网络在热轧宽厚板性能预测中的应用[J].鞍山钢铁学院学报,2002(01):24-28.
[11] 郭杏林,陈建林.基于神经网络技术的结构损伤探测[J].大连理工大学学报,2002(03):269-273.
[12] 吴凌云.基于神经网络的故障诊断专家系统[J].现代电子技术,2003(01):41-43.
[13] 郑晖,王昭东,王国栋,刘相华,张丕军,刘孝荣.利用人工神经网络模型预测SS400热轧板带力学性能[J].钢铁,2002(07):37-40,53.
[14] Qian Hancheng;Xia Bocai;Li Shangzheng .Fuzzy neural network modeling of material properties[J].Journal of Materials Processing Technology,2002(2/3):196-200.
[15] Cherian R P;Smith L N;Midha P S .A neural network approach for selection of powder metallurgy materials and process parameters[J].Artificial Intelligence in Engineering,2002,14:39.
[16] Chen Philip C L;Cao Yang;Steven R LeClair .Materials structure-property prediction using a self-architecting neural network[J].Journal of Alloys and Compounds,1998,279:30.
[17] 刘增良;刘有才.模糊逻辑与神经网络[M].北京:北京航空航天大学出版社,1996
[18] 关惠玲;韩捷.设备故障诊断专家系统原理及实践[M].北京:机械工业出版社,2000
[19] Lee J.A.;Almond D.P. .The use of neural networks for the prediction of fatigue lives of composite materials[J].Composites, Part A. Applied science and manufacturing,1999(10):1159-1169.
[20] Zenon Waszczyszyn;Leonard Ziemianski .Neural networks in mechanics of structures and materials-new results and prospects of applicaion[J].Computers and Structures,2001,79:2261.
[21] Ootao Y;Tanigawa Y;Nakamura T .Optimization of material composition of FGM hollow circular cylinder under thermal loading:a neural network approach[J].Composites Part B:Engineering,1999,30:415.
[22] Ootao Y;Ryuusuke Kawamura;Yoshinobu Tanigawa et al.Optimization of material composition of nonhomogeneous hollow sphere for thermal stress relaxation making use of nerual network[J].Computer Methods in Applied Mechanics and Engineering,1999,180:185.
[23] 党建武.神经网络技术及应用[M].北京:中国铁道出版社,2000
[24] 杨伟,倪黔东,吴军基.BP神经网络权值初始值与收敛性问题研究[J].电力系统及其自动化学报,2002(01):20-22.
[25] 宋桂荣,邢卫东.改进BP算法在故障诊断中的应用[J].沈阳工业大学学报,2001(03):252-254.
[26] 吴焱明;王纯贤;王治森 .改进的BP算法及其应用研究[J].合肥工业大学学报(自然科学版),1998,21(04):17.
[27] Murtagh F .Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering[J].Pattern Recognition Letters,1995,16:399.
[28] Christipher J Deschenes;Noonan J .Fuzzy Kohonen Network for the classification of Transients using the wavelet transform for feature extraction[J].Information Sciences,1995,87:247.
[29] Tzafestas SG.;Stamou GB. .AN IMPROVED NEURAL NETWORK FOR FUZZY REASONING IMPLEMENTATION[J].Mathematics and computers in simulation,1996(5/6):565-576.
[30] 王磊,袁慎芳.Kohonen神经网络在复合材料损伤主动监测技术中的应用[J].材料科学与工程,2002(04):513-516.
[31] 蒋东翔,王风雨,周明,倪维斗.模糊自组织神经网络在航空发动机故障诊断中的应用[J].航空动力学报,2001(01):80-82.
上一张 下一张
上一张 下一张
计量
  • 下载量()
  • 访问量()
文章评分
  • 您的评分:
  • 1
    0%
  • 2
    0%
  • 3
    0%
  • 4
    0%
  • 5
    0%