期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 4, 页码 2434-2442出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3093115
关键词
Adaptation models; Convergence; Fuzzy control; Neural networks; Adaptive systems; Mathematical model; Informatics; Adaptive fuzzy control; hybrid GNN-ZNN model; Lyapunov theory; matrix inverse
类别
资金
- National Natural Science Foundation of China [61866013, 61976089, 61966014]
- Natural Science Foundation of Hunan Province of China [2021JJ20005, 18A289]
- Hunan Provincial Science and Technology Project Foundation of China [2018TP1018, 2018RS3065]
In this article, a novel fuzzy adaptive GNN-ZNN (FA-GNN-ZNN) model is proposed for matrix inversion. By introducing a fuzzy adaptive control strategy, the FA-GNN-ZNN model can adaptively change its size according to the residual error, leading to better performance compared to the existing H-GNN-ZNN model under the same conditions. Additionally, different activation functions are applied to the FA-GNN-ZNN model to further improve its performance, as supported by theoretical analysis and comparative simulation results.
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.
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