期刊文献+

微分方程基于聚类分析的自适应差分格式

A Kind of the Difference Scheme Based on Cluster Analysis for Differential Equations
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摘要 通过聚类分析找出一般差分格式的数值解出现数值信息波动大的区域,自适应地进行网格加密,构造出高精度的自适应差分格式.数值试验结果表明,这种新算法较一般差分格式能显著地减少数据存储量和计算量,提高差分格式的稳定性和数值解的精度. The fields of the fluctuation information of numerical solutions of the difference scheme for differential equations based on a cluster analysis are found,the node points of these fields are self-adaptively refined, and some new self-adaptive difference schemes with high-accuracy for differential equations are obtained. Numerical results show that compared with general difference schemes, the new difference scheme algorithms can significantly reduce data storage, computing time and improve the stability of difference schemes and the accuracy of numerical solutions.
出处 《内蒙古大学学报(自然科学版)》 CAS CSCD 北大核心 2011年第1期32-39,共8页 Journal of Inner Mongolia University:Natural Science Edition
基金 国家自然科学基金项目资助(No.10871022) 中央高校基本业务费专项资金资助(No.2009-2-05)
关键词 聚类分析 差分格式 数据预处理 自适应 cluster analysis difference scheme data preprocess self-adapted
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参考文献8

  • 1胡建伟.微分方程数值解法[M].北京:科学出版社,1999.
  • 2杨琦,万华.一种调整型径向基神经网络偏微分方程解法[J].弹箭与制导学报,2006,26(3):192-194. 被引量:2
  • 3Li J Y. Numerical solution of elliptic partial differential equation using radial basis function neural networks[J]. Neural Networks, 2003,16 : 729-734.
  • 4富坤,汪友华,何平,沈雪勤.应用支持向量机回归求解不规则边界边值问题[J].河北省科学院学报,2005,22(z1):92-93. 被引量:2
  • 5Shao Yuanhai,Feng Yining,Chen Jing,et al. Density Clustering Based SVM and Its Application to Polyadenylation Signals[C]. Proc. of the Third International Symposium on OSB, Zhangjiaiie, China. Zhangiiajie: ORSC APORC, 2009 : 117-122.
  • 6Aderberg M R. Cluster Analysis for Applications[M]. New York: Academic Press, 1973 : 134-148.
  • 7Hartigan J. Clustering Algorithms [M]. New York: Wiley, 1975 : 123-135.
  • 8Zhang T,Ramakrishnan R, Livny M. BIRCH:An Efficient Data Clustering Method for Very Large Databases [C]. Proc. ACM-SGMOD Int. Conf. Management of Data, Montreal,Canada, 1996:103-114.

二级参考文献13

  • 1[1]A J Smola, B. Scholkopf.. A Tutorial on Support Vector Regression. http://www. neurocolt. com, 2000
  • 2[2]Isaac Elias Lagaris, Aristidis Likas. Neural- Network Methods for Boundary Value Problems with Irregular Boundaries.IEEE Transactions on Neural Network. 2000, 11(5): 1041 -1049
  • 3[4]Marinaro M, Scarpetta S. On - line learning in RBF neural networks: A stochastic approach. Neural Networks, 2000,13: 719 - 729
  • 4[5]David Andre. Exploring Online Support Vector Regression.http://www. es. berkeley. edu/~ dandre, 1999
  • 5[6]John C Platt. Fast Training of Support Vector Machines using Sequential Minimal Optimization. MITPress, 1999
  • 6张学工.统计学习理论的本质[M].北京:清华大学出版社,2000..
  • 7E J Kansa.Multi-quadrics-a scattered data approximation scheme with applications to computational fluid dynamics-Ⅱ[J].Computers in Mathematical Applications,1998,(9):127-145.
  • 8C Franke and R Schaback.Convergence orders of meshless collocation methods using radial basis functions[J].Advances in Computational Mathematics,1997,(8):381-399.
  • 9C Franke,and R Schaback.Solving partial differential equations by collocation using radial basis functions[J].Applied Mathematics and Computation,1998,(93):73-82.
  • 10A Esposito and M Marinaro.Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm[J].Neural Networks,2000,(13):651-665.

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