Name
Abstract | ||
|---|---|---|
Since the solution of time-variant nonlinear inequality systems is trapped by the convergence performance of the models, this paper explores an enhanced nonlinear sign-bi-power activation function (AF) and further obtains a zeroing neural network (ZNN) model for solving time-variant nonlinear inequality systems, which is called nonlinear activated finite-time convergent zeroing neural network (NAFTCZNN). The strict theoretical analysis together with two theorems are given to demonstrate the enhanced convergence performance of the NAFTCZNN model. Furthermore, the stability and upper bound of convergent time of the NAFTCZNN model are analyzed and estimated in the theorems, which is more stable and less conservative than the ZNN models using the common sign-bi-power AFs. Numerical example results further validate the effectiveness and excellence of the NAFTCZNN model in terms of solving the time-variant nonlinear inequality systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2022.05.067 | Neurocomputing |
Keywords | DocType | Volume |
Zeroing neural network (ZNN),Time-variant nonlinear inequality systems,Activation function,Finite-time convergence | Journal | 500 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Xiao | 1 | 94 | 15.07 |
| Wentong Song | 2 | 0 | 0.34 |
| Lei Jia | 3 | 1 | 3.39 |
| Xiaopeng Li | 4 | 171 | 32.15 |