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2008 IEEE International Conference on Networking, Sensing and Control

DOI: 10.1109/icnsc.2008.4525454

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MATLAB Simulink Modeling and Simulation of Zhang Neural Network for Online Time-Varying Matrix Inversion

Proceedings article published in 2008 by Yunong Zhang, Xiaojiao Guo, Weimu Ma, Weimu, Ke Chen, Binghuang Cai ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Recently, a special kind of recurrent neural networks (RNN) with implicit dynamics has been proposed by Zhang et al for online time-varying problems solving (such as time-varying matrix inversion). Such a neural-dynamic system is elegantly designed by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function. Its computational error can be made decrease to zero globally and exponentially. For the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, we investigate in this paper the MATLAB Simulink modeling and simulative verification of such a Zhang neural network (ZNN). By using click-and-drag mouse operations, it is easier to model and simulate in comparison with MATLAB coding. Both convergence and robustness properties of such a ZNN model are analyzed, which substantiate the effectiveness of Zhang neural network on inverting the time-varying matrices.