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Novel Discrete-Time Zhang Neural Network for Time-Varying Matrix Inversion.
- Source :
- IEEE Transactions on Systems, Man & Cybernetics. Systems; Aug2017, Vol. 47 Issue 8, p2301-2310, 10p
- Publication Year :
- 2017
-
Abstract
- 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 discrete-time 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. [ABSTRACT FROM PUBLISHER]
- Subjects :
- ARTIFICIAL neural networks
MATRIX inversion
TAYLOR'S series
Subjects
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 47
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 124252392
- Full Text :
- https://doi.org/10.1109/TSMC.2017.2656941