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Neural network approach for solving nonsingular multi-linear tensor systems.
- Source :
-
Journal of Computational & Applied Mathematics . Apr2020, Vol. 368, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- The main propose of this paper is to develop two neural network models for solving nonsingular multi-linear tensor system. Theoretical analysis shows that each of the neural network models ensures the convergence performance. For possible hardware implementation of the proposed neural network models, based on digital circuits, we adopt the Euler-type difference rule to discretize the corresponding Gradient neural network (GNN) models. The computer simulation results further substantiate that the models can solve a multi-linear system with nonsingular tensors. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*DIGITAL electronics
*COMPUTER simulation
Subjects
Details
- Language :
- English
- ISSN :
- 03770427
- Volume :
- 368
- Database :
- Academic Search Index
- Journal :
- Journal of Computational & Applied Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 140096696
- Full Text :
- https://doi.org/10.1016/j.cam.2019.112569