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Design, Analysis, and Representation of Novel Five-Step DTZD Algorithm for Time-Varying Nonlinear Optimization.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Sep2018, Vol. 29 Issue 9, p4248-4260, 13p
- Publication Year :
- 2018
-
Abstract
- Continuous-time and discrete-time forms of Zhang dynamics (ZD) for time-varying nonlinear optimization have been developed recently. In this paper, a novel discrete-time ZD (DTZD) algorithm is proposed and investigated based on the previous research. Specifically, the DTZD algorithm for time-varying nonlinear optimization is developed by adopting a new Taylor-type difference rule. This algorithm is a five-step iteration process, and thus, is referred to as the five-step DTZD algorithm in this paper. Theoretical analysis and results of the proposed five-step DTZD algorithm are presented to highlight its excellent computational performance. The geometric representation of the proposed algorithm for time-varying nonlinear optimization is also provided. Comparative numerical results are illustrated with four examples to substantiate the efficacy and superiority of the proposed five-step DTZD algorithm for time-varying nonlinear optimization compared with the previous DTZD algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- TIME-varying systems
DISCRETE-time systems
NONLINEAR dynamical systems
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 131486962
- Full Text :
- https://doi.org/10.1109/TNNLS.2017.2761443