期刊文献+

神经网络集成及研究进展 被引量:1

The Neural Network Ensemble and its Research Developing
下载PDF
导出
摘要 神经网络集成是机器学习和神经计算重要研究领域,通过训练有限个神经网络个体,并将其结论进行适当的合成,可以极大提高学习系统的泛化能力,已经成为一种有广阔应用前景的工程化神经计算技术。本文讨论了神经网络集成的理论方法及其研究进展,并对该领域进一步研究的问题进行了探讨。 The Neural Network ensemble is an applied important research in machine learning and Neuro-computing, because it can significantly improve the generalization ability of neural network through ensembling a number of neural networks, i. e. training many neural networks and then combining their prediction. It has the broad application prospect since this technology behaves remarkably well based system. Recently it has become a very hot topic research. The paper discusses the development, theory foundation and at last, it shows clearly the future and the development of the Neural Network ensemble .
出处 《柳州师专学报》 2007年第4期118-122,共5页 Journal of Liuzhou Teachers College
基金 广西教育厅项目(200508234)
关键词 神经网络 神经网络集成 泛化误差 neural network neural network ensemble generation error
  • 相关文献

参考文献42

  • 1SIMon HYKIN.神经网络原理[M].叶世伟,史忠植译.北京:机械工业出版社,2004.
  • 2T.Martin Hagan,B.Howard Demuth,H Mark Beale.神经网络设计[M].戴葵,译.北京:机械工业出版社,2002.
  • 3R Hecht Nielsen.Neurocomputing,Reading[M].MA:Addison-Wesley,1990.
  • 4Kearns M.Valiant L G.Learning Boolean formulae or factoring[M].Aiken Computation Laboratory,Harvard University,Cambridge,MA,Technical Report:TR-1488,1985.
  • 5Anthony M.Probabilistic analysis of learning in artificial neural networks:The PAC model and its variants[J].Neura1 Computing Surveys.1997(1):1-47.
  • 6Schapire R E.The strength of weak learn ability[J].Machine Learning,1990,5(2):197-227.
  • 7Hansen L K,Salamon P.Neural Network Ensembles[J].IEEE Transaction on Pattern Analysis and Machine Intelligence.1990,12(10):993-1001.
  • 8Sollich P,Krogh A.Learning with ensembles;How over-fitting can he useful[C].In:Touretzky D,Mozer M,Hasselmo Meds.Adances in Neural Information Processing Systems 8,Cambridge,MA:MIT Press.1996:190-196.
  • 9Opitz D,Maclin R.Popular ensemble methods:An empirical study[J].Journal of Artificial Intelligence Research,1999,11:l69-198.
  • 10Cooper L N.Hybrid neural network architectures:Equilibrium systems that pay attention[C].In:Mammon R J,Zeevi Y Y eds.Neural Networks:Theory and Applications,San Diego.CA:Academic Press,l991:8l-96.

二级参考文献67

  • 1吴建生,金龙,农吉夫.遗传算法BP神经网络的预报研究和应用[J].数学的实践与认识,2005,35(1):83-88. 被引量:52
  • 2颜延虎,钟秉林,黄仁,万德均.神经网络技术及其在旋转机械故障诊断中的应用[J].振动工程学报,1993,6(3):205-212. 被引量:23
  • 3胡方明,简琴,张秀君.基于BP神经网络的车型分类器[J].西安电子科技大学学报,2005,32(3):439-442. 被引量:22
  • 4沈掌泉,孔繁胜.基于广义回归网络的动态权重回归型神经网络集成方法研究[J].计算机应用研究,2005,22(12):41-43. 被引量:6
  • 5(英)P.J.达夫勒 姜建国(译).电机的状态监测[M].北京:水利电力出版社,1992..
  • 6朱启敏.电机故障诊断技术[M].北京:机械工业出版社,1996..
  • 7朱启敏.电动机故障诊断知识工程(学位论文)[M].沈阳:东北大学,1997..
  • 8吴建鑫 周志华 陈世福.神经网络集成综述[A].中国人工智能学会.中国人工智能学会第九届全国学术年会论文集[C].北京:北京邮电大学出版社,2001.455--458.
  • 9Algis Garliauskas. Neural network chaos and computational algorithms of forecast in finance[J]. Proceedings of the IEEE International Conference on System, Man and Cybernetics, 1999, 2 : 638-- 643.
  • 10Burgess A N, Bumm D W, Refenes A-P N. Neural networks with error feedback terms for financial time series modelling[A]. Prceedings of the Neural Network Conference EC], 1997, IOP Publishing Ltd and Dxford University Press, 1997. 65--75.

共引文献186

同被引文献9

  • 1WOLPAW J R, BIRBAUMER N, HEETDERKS W J, et al. Brain-computer interface technology: a review of the first international meeting[ J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8 (2) : 164-173.
  • 2WOLPAW J R, BIRBAUMER N, MAFARLAND D J, et al. Brain-computer interface for communication and control[ J]. Clinical Neurop-hysiology, 2002, 113 (6) : 767-791.
  • 3PINCUS S M. Approximate entropy as a complexity measure [ J]. Chaos, 1995, 5( 1 ) : 110-117.
  • 4ROBERTS S J, PENNY W, REZEK I. Temporal and spatial complexity measures for EEG-based brain-computer interfacing [ J]. Medical and Biological Engineering and Computing, 1998, 37 (1) : 93-99.
  • 5HANSEN L K, SALAMON P. Neural network ensembles [ J~. IEEE Trans Pattern Analysis and Machine Intelligence, 1990, 12(10) : 993-1001.
  • 6KAMOUSI B, LIU Z, HE B. Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis[ J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005, 13 (2) : 166- 171.
  • 7SCHLOGL A, LUGGER K, PFURTSCHELLER G, et al. Using adaptive autoregressive parameters for a brain-computer- interface experiment[ C] ///The 19'h Annual International Conference IEEE/EMBS. Chicago: IEEE, 1997, 23(5): 130- 1335. 63-67.
  • 8钱博,李燕萍,唐振民,徐利敏.基于神经网络集成的说话人识别算法仿真研究[J].系统仿真学报,2008,20(5):1285-1288. 被引量:5
  • 9陈梅兰.神经网络集成的设计与应用[J].现代计算机,2008,14(4):28-31. 被引量:2

引证文献1

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部