XIAO Lin, LU Rongbo. A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations[J]. Chinese Journal of Electronics, 2017, 26(6): 1194-1197. doi: 10.1049/cje.2017.06.007
Citation: XIAO Lin, LU Rongbo. A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations[J]. Chinese Journal of Electronics, 2017, 26(6): 1194-1197. doi: 10.1049/cje.2017.06.007

A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations

doi: 10.1049/cje.2017.06.007
Funds:  This work is supported by the National Natural Science Foundation of China (No.61503152, No.61563017, No.61363073), the Natural Science Foundation of Hunan Province, China (No.2016JJ2101, No.2017JJ3258), and the Research Foundation of Education Bureau of Hunan Province, China (No.15B192).
  • Received Date: 2015-09-17
  • Rev Recd Date: 2015-12-18
  • Publish Date: 2017-11-10
  • This paper concerns online solution of complex-valued linear matrix equations in the complex domain. Differing from the real-valued neural network, which is only designed for solving real-valued linear matrix equations in the real domain, a fully complex-valued Gradient neural network (GNN) is developed for computing complex-valued linear matrix equations. The fully complex-valued GNN model has the merit of reducing the unnecessary complexities in theoretical analysis and realtime computation, as compared to the real-valued neural network. Besides, the convergence analysis of the proposed complex-valued GNN model is presented, and simulation experiments are performed to substantiate the effectiveness and superiority of the proposed complex-valued GNN model for online computing the complex-valued linear matrix equations in the complex domain.
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