期刊文献+

一个奇异值神经网络模型及其稳定性分析

A Singular Value Neural Network Model and Its Stability Analysis
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摘要 提出了一个新的计算实矩阵最大奇异值的神经网络模型,通过求解其微分方程模型的解析解,给出了该模型的渐近稳定性分析,最后给出了数值试验结果,进一步验证了理论分析的正确性. A novel neural network model for computing the largest singular value of a given real matrix is proposed. By presenting the analytical solution of the differential equations model, asymptotic stability is derived. Finally, a numerical experiment is provided, which further verifies correctness of the theoretical analysis.
出处 《郑州大学学报(理学版)》 CAS 2008年第3期88-92,共5页 Journal of Zhengzhou University:Natural Science Edition
基金 大连民族学院"太阳鸟"学生科研项目
关键词 神经网络 奇异值 收敛性 渐近稳定性 neural network singular value convergence asymptotic stability
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参考文献4

  • 1Wang J,Wu G. Recurrent neural networks for LU decomposition and Cholesky factorization[J]. Math Comput Model ling, 1993,18(6):1- 8.
  • 2Zhang Y, Yan F, Tang H J. Neural networks based approach for computing eigenvectors and eigenvalues of symmetric matrix[J]. Comp and Math with Appl, 2004, 47(4): 1155-1164.
  • 3Liu Y G, You Z S, Cao L P. A simple functional neural network for computing the largest and smallest eigenvalues and corresponding eigenvectors of a real symmetric matrix[J]. Neurocomputing, 2005, 67:369 -383.
  • 4Miller R K, Michel A N. Ordinary Differential Equations[M]. New York: Oversea Publishing House, 1982.

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