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• W3089173872 endingPage "106735" @default.
• W3089173872 startingPage "106735" @default.
• W3089173872 abstract "By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN) models are designed and studied to efficiently tackle the time-varying matrix pseudoinversion problem. Compared with ZNN activated by previously presented activation functions, these two simplified finite-time ZNN (SFTZNN) models (called SFTZNN1 and SFTZNN2) not only achieve faster finite-time convergence, but also possess better robustness. In addition, the SFTZNN1 and SFTZNN2 models have simpler structure compared with the widely used sign-bi-power activated ZNN model. Theoretical analysis is presented to obtain the maximum convergence time for the SFTZNN models in ideal conditions. Besides, when external perturbations are injected into the proposed SFTZNN models, upper bounds of the steady-state residual error are theoretically calculated. Comparative simulations and one engineering application case validate the feasibility and superiority of the two new SFTZNN models when solving time-varying matrix pseudoinversion." @default.
• W3089173872 created "2020-10-01" @default.
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• W3089173872 date "2021-01-01" @default.
• W3089173872 modified "2023-10-16" @default.
• W3089173872 title "Performance analysis of nonlinear activated zeroing neural networks for time-varying matrix pseudoinversion with application" @default.
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• W3089173872 doi "https://doi.org/10.1016/j.asoc.2020.106735" @default.
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