期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 10, 页码 6231-6241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2966544
关键词
Mathematical model; Computational modeling; Informatics; Convergence; Neural networks; Numerical models; Symmetric matrices; Broyden-Fletcher-Goldfarb-Shanno (BF-GS) method; matrix inversion; Sylvester equation; theoretical analyses
类别
资金
- National Natural Science Foundation of China [61703189]
- National Key Research and Development Program of China [2017YFE0118900]
- Natural Science Foundation of Gansu Province, China [18JR3RA264]
- Sichuan Science and Technology Program [19YYJC1656]
- Fundamental Research Funds for the Central Universities [lzujbky-2019-89, TII-19-0050]
In this article, a neural dynamics model is constructed and investigated for solving time-dependent Sylvester equation with matrix inversion involved in the solving process. Besides, to eliminate the matrix inversion in the model, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno method is leveraged to construct a new model. Moreover, the global convergence performance and the effectiveness of the two discrete computational models are testified by providing theoretical analyses and numerical experiments with comparisons to the existing solutions, respectively. Two applications to robotics and the multiple-input multiple-output system are given to elucidate the feasibility of the proposed models for solving time-dependent Sylvester equation.
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