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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

December 2016, Vol. 28, No. 12, Pages 2790-2824
(doi: 10.1162/NECO_a_00866)
© 2016 Massachusetts Institute of Technology
Complex Neural Network Models for Time-Varying Drazin Inverse
Article PDF (2.93 MB)
Abstract

Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.