A concise functional neural network computing the largest modulus eigenvalues and their corresponding eigenvectors of a real skew matrix

DOI10.1016/j.tcs.2006.05.026zbMath1119.68153OpenAlexW2026057672MaRDI QIDQ857390

Yiguang Liu, Liping Cao, Zhisheng You

Publication date: 14 December 2006

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2006.05.026

zbMATH Keywords

eigenvalueseigenvectorsfunctional neural networkreal skew matrix

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Another neural network based approach for computing eigenvalues and eigenvectors of real skew-symmetric matrices

Cites Work

• Training spatially homogeneous fully recurrent neural networks in eigenvalue space.
• A neural network for computing eigenvectors and eigenvalues
• Neural networks for computing eigenvalues and eigenvectors
• Complex recurrent neural network for computing the inverse and pseudo-inverse of the complex matrix
• On an integrable discretization of the Rayleigh quotient gradient system and the power method with a shift
• A matrix inverse eigenvalue problem and its application
• Estimation of singular values of very large matrices using random sampling
• Neural networks based approach for computing eigenvectors and eigenvalues of symmetric matrix
• A principal component analysis algorithm with invariant norm
• Real-time neural computation of the eigenvector corresponding to the largest eigenvalue of positive matrix
• A nonlinear neural network model of mixture of local principal component analysis: Application to handwritten digits recognition
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