Super finite-Time variable parameter ZNN models for time-Variant linear matrix inequality

Stuck in the speed and dimensionality of settling time-variant linear matrix inequality (LMI), this paper for the first time proposes two finite-time variable parameter zeroing neural network (FTVPZNN) models to settle the time-variant LMI. The first model is called the FTVPZNN-C model activated by the conventional sign-bi-power (S-B-P) function. The second model is called the FTVPZNN-T model activated by a tunable parameter S-B-P function. Different from the finite-time fixed-value zeroing neural network (FTFZNN) model, the proposed FTVPZNN models with variable parameters have better convergence performance and smaller upper bounds of finite-time convergence. Three theorems are presented to guarantee the stability and finite-time convergence of the FTVPZNN models. Especially, through detailed theoretical analysis and calculations, the finite-time convergence upper bounds of the proposed FTVPZNN models are obtained. Finally, a numerical simulative example is given to affirm the effectiveness and excellent convergent performance of the proposed models for settling the time-variant LMI.