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A Complex Neural Network Algorithm for Computing the Largest Real Part Eigenvalue and the corresponding Eigenvector of a Real Matrix

2016
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Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
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unpublished

In this study, we propose a novel complex neural network algorithm, which extends the neural network based approaches that can asymptotically compute the largest or smallest eigenvalues and the corresponding eigenvectors of real symmetric matrices, to the case of directly calculating the largest real part eigenvalue and the corresponding eigenvector of a real matrix. The proposed neural network algorithm is described by a group of complex differential equations, which is deduced from the

doi:10.2991/icsma-16.2016.100
fatcat:2ss25nlnh5holhoqtya2yzmlsa