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Design and Analysis of a Hybrid GNN-ZNN Model With a Fuzzy Adaptive Factor for Matrix Inversion | IEEE Journals & Magazine | IEEE Xplore

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...Show More

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.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 18, Issue: 4, April 2022)
Page(s): 2434 - 2442
Date of Publication: 29 June 2021

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