Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

IEEE Access

Bolin Liao¹  Jialiang Chen¹  Yihui Lei² 

[1] College of Information Science and Engineering, Jishou University, Jishou, China;College of Mathematics and Statistics, Jishou University, Jishou, China;

关键词: Zeroing neural network;    time-dependent;    finite-time convergence;    complex-valued;    anti-noise;    matrix inverse;   

DOI  :  10.1109/ACCESS.2020.2994102

来源: DOAJ

【 摘 要 】

Computing complex-valued time-dependent matrix inverse is an important procedure in statistics and control theory. Zeroing neural network (ZNN) becomes an effective method for solving time-dependent issues. This paper proposes a complex-valued synthetical ZNN (CVSZNN) model for computing complex-valued time-dependent matrix inverse within different noise environments. Two important property is synthetically dissected in the construction process of CVSZNN model. First, the finite-time convergence, to ensure the efficiency of CVSZNN model for solving complex-valued time-dependent issue. Secondly, the anti-noise property, to guarantee the robustness of CVSZNN model in presence of noises. Two important aspects above have been validated by theoretical analysis and numerical experiments. Furthermore, the impact of parameters values is investigated based on the experimental results. As compared with the existing ZNN models for computing complex-valued time-dependent matrix inverse, the superiority of the proposed CVSZNN model is displayed fully.

【 授权许可】

Unknown