JACIII Vol.20 No.1 pp. 132-138
doi: 10.20965/jaciii.2016.p0132


Novel Complex-Valued Neural Network for Dynamic Complex-Valued Matrix Inversion

Bolin Liao*, Lin Xiao*, Jie Jin*, Lei Ding*, and Mei Liu**

*College of Information Science and Engineering, Jishou University
Jishou 416000, China

**College of Physics, Mechanical and Electrical Engineering, Jishou University
Jishou 416000, China

November 10, 2015
December 10, 2015
Online released:
January 19, 2016
January 20, 2016
complex-valued matrix inverse, gradient neural network, Zhang neural network, dynamic
Static matrix inverse solving has been studied for many years. In this paper, we aim at solving a dynamic complex-valued matrix inverse. Specifically, based on the artful combination of a conventional gradient neural network and the recently-proposed Zhang neural network, a novel complex-valued neural network model is presented and investigated for computing the dynamic complex-valued matrix inverse in real time. A hardware implementation structure is also offered. Moreover, both theoretical analysis and simulation results substantiate the effectiveness and advantages of the proposed recurrent neural network model for dynamic complex-valued matrix inversion.
Cite this article as:
B. Liao, L. Xiao, J. Jin, L. Ding, and M. Liu, “Novel Complex-Valued Neural Network for Dynamic Complex-Valued Matrix Inversion,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.1, pp. 132-138, 2016.
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