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A Fuzzy Adaptive Zeroing Neural Network Model With Event-Triggered Control for Time-Varying Matrix Inversion | IEEE Journals & Magazine | IEEE Xplore

A Fuzzy Adaptive Zeroing Neural Network Model With Event-Triggered Control for Time-Varying Matrix Inversion


Abstract:

Time-varying matrix inversion (TVMI) is a basic mathematical problem, which is widely involved in many scientific fields. In this article, an event-triggered control fuzz...Show More

Abstract:

Time-varying matrix inversion (TVMI) is a basic mathematical problem, which is widely involved in many scientific fields. In this article, an event-triggered control fuzzy adaptive zeroing neural network (ETC-FAZNN) model is proposed for solving the TVMI problem, where the fuzzy adaptive convergence parameter (FACP) is got by the redesigned fuzzy logic system, which makes the ETC-FAZNN model adaptive. Meanwhile, the event-triggered control is introduced to control the update of the FACP, which improves the calculation speed of the ETC-FAZNN model. Moreover, a novel activation function called segmented predefined-time activation function is put forward in this article to improve the convergence and robustness of the ETC-FAZNN model. Theoretical analysis and simulation experiments reveal that the ETC-FAZNN model can realize stability, predefined-time convergence, robustness, and adaptability performances in solving the TVMI problem.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 31, Issue: 11, November 2023)
Page(s): 3974 - 3983
Date of Publication: 03 May 2023

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References

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