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Design, Analysis, and Representation of Novel Five-Step DTZD Algorithm for Time-Varying Nonlinear Optimization.

Authors :
Guo, Dongsheng
Yan, Laicheng
Nie, Zhuoyun
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]

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