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• W4382929897 abstract "At present, there are few studies on solving time-variant linear equality and inequality systems (TVLEIS) under noise interference, and the numerical algorithm has limitations in solving the TVLEIS problems. Therefore, to determine the online solution of the TVLEIS in a complex environment, two prescribed-time robust zeroing neural network (PTRZNN) models are proposed, investigated, and verified in this paper. The PTRZNN models have a faster convergence rate and superior robustness compared with other zeroing neural network models activated by common activation functions. In addition, the detailed theoretical derivation of the prescribed-time convergence and robustness of the PTRZNN models is provided. The effectiveness and superiority of the PTRZNN models for determining the TVLEIS are further demonstrated by simulation results. It is worth mentioning that the design idea of the PTRZNN models is applied to the multi-agent system, which shows the practical value of the PTRZNN models." @default.
• W4382929897 created "2023-07-04" @default.
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• W4382929897 date "2023-09-01" @default.
• W4382929897 modified "2023-10-18" @default.
• W4382929897 title "Prescribed-time robust ZNN models for solving equality and inequality systems" @default.
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• W4382929897 doi "https://doi.org/10.1016/j.jfranklin.2023.06.044" @default.
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