Name
Title | ||
|---|---|---|
New error function designs for finite-time ZNN models with application to dynamic matrix inversion |
Abstract | ||
|---|---|---|
The zeroing neural network (ZNN), as a special kind of recurrent neural network (RNN), is often utilized to solve dynamic matrix inversion problems in the many fields recently. In this work, two finite-time ZNN (termed as ZNN-A and ZNN-B) models with the sign-bi-power (SBP) activation function are proposed by designing two novel error functions. The theoretical analysis shows the superior stability and finite-time convergence properties of the ZNN-A and ZNN-B models. Furthermore, three simulative examples show the effectiveness of the proposed ZNN-A and ZNN-B models for finding dynamic matrix inversion and the correctness of the corresponding theorems. To reveal the superior performance of the proposed ZNN-A and ZNN-B models with SBP activation function, the standard ZNN model with the linear activation function is comparatively applied in experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.02.121 | Neurocomputing |
Keywords | DocType | Volume |
Zeroing neural network (ZNN),Activation function,Finite-time convergence,Dynamic matrix inversion,Error Functions | Journal | 402 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Xiao | 1 | 94 | 15.07 |
| Haiyan Tan | 2 | 1 | 1.70 |
| Lei Jia | 3 | 1 | 3.39 |
| Jianhua Dai | 4 | 896 | 51.62 |
| Yongsheng Zhang | 5 | 204 | 43.58 |