期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 7, 页码 4447-4455出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3129526
关键词
Tensors; Convergence; Computational modeling; Informatics; Mathematical models; Analytical models; Robustness; Dynamic-parameter; finite-time convergence; time-varying tensor inversion (TVTI); zeroing neural-network (ZNN)
类别
资金
- National Natural Science Foundation of China [61866013, 61976089, 61966014]
- Natural Science Foundation of Hunan Province of China [2021JJ20005, 18A289, 2018TP1018, 2018RS3065]
This article introduces the background and challenges of the time-varying tensor inversion problem, and proposes a novel DP-ZNN model to solve this problem. Through theoretical analysis and experimental verification, it is proved that the model has superior convergence performance.
Time-varying tensor inversion (TVTI) problem is a kind of general time-varying inversion problem in mathematics because scalars, vectors, and matrices can all be represented by tensors. The TVTI problem is based on a novel tensor product [termed the TensorFlow (TF) product], which is extracted from the TF. For solving such a prevalent problem, the matricization of the TF product is defined, and a novel dynamic-parameter zeroing neural-network (DP-ZNN) model is proposed by combining a ZNN design formula and a dynamic-parameter. The global convergence and the upper bound of finite-time convergence of the DP-ZNN model are analyzed theoretically. For highlighting the superior convergence performance and excellent efficiency of the DP-ZNN model in solving the TVTI problem, three comparative experiments are presented in this article. Experimental results show that the DP-ZNN model has remarkable convergent speciality.
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