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1 Bulletin of the Polish Academy of Sciences. Technical Sciences

1 2022

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Robust zeroing neural networks with two novel power-versatile activation functions for solving dynamic Sylvester equation

Zhou Peng, Tan Mingtao

Bulletin of the Polish Academy of Sciences. Technical Sciences

2022

Vol. 70, nr 3

art. no. e141307

In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving of the dynamic Sylvester equation (DSE), by introducing two novel power-versatile activation functions (PVAF), respectively. Differing from most of the zeroing neural network (ZNN) models activated by recently reported activation functions (AF), both of the presented PVAF-based RZNN models can achieve predefined time convergence in noise and disturbance polluted environment. Compared with the exponential and finite-time convergent ZNN models, the most important improvement of the proposed RZNN models is their fixed-time convergence. Their effectiveness and stability are analyzed in theory and demonstrated through numerical and experimental examples.