期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 47, 期 8, 页码 2301-2310出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2656941
关键词
Difference rule; discrete-time Zhang neural network (DTZNN); Taylor series expansion; theoretical results; time-varying matrix inversion
资金
- National Natural Science Foundation of China [61603143]
- Natural Science Foundation of Fujian Province [2016J01307]
- Promotion Program for Young and Middle-Aged Teacher in Science and Technology Research of Huaqiao University [ZQN-YX402]
- Scientific Research Funds of Huaqiao University [15BS410]
In the previous work, Zhang et al. developed a special type of recurrent neural networks called Zhang neural network (ZNN) with continuous-time and discrete-time forms for time-varying matrix inversion. In this paper, a novel discretetime ZNN (DTZNN) model for time-varying matrix inversion is proposed and investigated. Specifically, a new numerical difference rule based on Taylor series expansion is established in this paper for first-order derivative approximation. Then, by exploiting this Taylor-type difference rule, the novel DTZNN model, which is a five-step iteration algorithm, is thus proposed for time-varying matrix inversion. Theoretical results are also presented for the proposed DTZNN model to show its excellent computational property. Comparative numerical results with three illustrative examples further substantiate the efficacy and superiority of the proposed DTZNN model for time-varying matrix inversion compared with previous DTZNN models.
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