Neural network for computing pseudoinverses and outer inverses of complex-valued matrices

Neural network for computing pseudoinverses and outer inverses of complex-valued matrices

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Article ID: iaor201530250
Volume: 273
Start Page Number: 1107
End Page Number: 1121
Publication Date: Jan 2016
Journal: Applied Mathematics and Computation
Authors: , ,
Keywords: neural networks, matrices, computers: information, optimization
Abstract:

We propose two continuous‐time neural networks for computing generalized inverses of complex‐valued matrices with rank‐deficient cases. The first of them is applicable in the pseudoinverse computation and the second one is applicable in construction of outer inverses. The proposed continuous‐time neural networks have a low complexity of implementation and they are proved to be globally convergent without any condition. Compared with the existing algorithms for computing the pseudoinverse and outer inverses of matrices, the global convergence of the proposed continuous‐time neural networks is analyzed in the complex domain. Effectiveness of the proposed continuous‐time neural networks is evaluated numerically via examples.

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