Neural-network-based approach for extracting eigenvectors and eigenvalues of real normal matrices and some extension to real matrices

DOI10.1155/2013/597628zbMath1266.65062OpenAlexW1966353092WikidataQ59003236 ScholiaQ59003236MaRDI QIDQ2375598

Shirong Bu, Zhengxiang Luo, Ying Tang, Xiongfei Zou, Shou-ming Zhong

Publication date: 14 June 2013

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/597628

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Neural networks for/in biological studies, artificial life and related topics (92B20)

Cites Work

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• Training spatially homogeneous fully recurrent neural networks in eigenvalue space.
• A new approach to multiple class pattern classification with random matrices
• A norm-reducing Jacobi-like algorithm for the eigenvalues of non-normal matrices
• Neural networks based approach for computing eigenvectors and eigenvalues of symmetric matrix
• A recurrent neural network computing the largest imaginary or real part of eigenvalues of real matrices
• A functional neural network computing some eigenvalues and eigenvectors of a special real matrix
• On the Eigenspectrum of the Gram Matrix and the Generalization Error of Kernel-PCA
• Computing a Lower Bound of the Smallest Eigenvalue of a Symmetric Positive-Definite Toeplitz Matrix
• An Extended Projection Neural Network for Constrained Optimization

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