@ARTICLE{Zhou_Peng_Robust_2022,
 author={Zhou, Peng and Tan, Mingtao},
 volume={70},
 number={3},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e141307},
 howpublished={online},
 year={2022},
 abstract={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.},
 type={Article},
 title={Robust zeroing neural networks with two novel power-versatile activation functions for solving dynamic Sylvester equation},
 URL={http://www.journals.pan.pl/Content/123162/PDF/2937_BPASTS_2022_70_3.pdf},
 doi={10.24425/bpasts.2022.141307},
 keywords={recurrent neural network (RNN), zeroing neural network (ZNN), RZNN, fixed-time convergence},
}