Name
Title | ||
|---|---|---|
Design and Analysis of a Hybrid GNN-ZNN Model With a Fuzzy Adaptive Factor for Matrix Inversion |
Abstract | ||
|---|---|---|
Motivated from the convergence capability achieved by gradient neural network (GNN) and zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H-GNN-ZNN) model is proposed by introducing a fuzzy adaptive control strategy to generate a fuzzy adaptive factor that can change its size adaptively according to the residual error. Due to its fuzzy adaptability, this novel model is called the fuzzy adaptive GNN-ZNN (FA-GNN-ZNN) model for presentation convenience. We prove that the FA-GNN-ZNN model has the better performance than the existing H-GNN-ZNN model under the same conditions. In addition, different activation functions are applied to the FA-GNN-ZNN model to improve its performance further, and the corresponding theoretical analysis is given. Finally, comparative simulation results demonstrate the validity and superiority of the FA-GNN-ZNN model for matrix inversion. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TII.2021.3093115 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Adaptive fuzzy control,hybrid GNN-ZNN model,Lyapunov theory,matrix inverse | Journal | 18 |
Issue | ISSN | Citations |
4 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhua Dai | 1 | 896 | 51.62 |
| Yuanmeng Chen | 2 | 0 | 0.34 |
| Lin Xiao | 3 | 94 | 15.07 |
| Lei Jia | 4 | 10 | 3.82 |
| Yongjun He | 5 | 0 | 0.68 |