An efficient second‐order neural network model for computing the Moore–Penrose inverse of matrices
Abstract The computation of the Moore–Penrose inverse is widely encountered in science and engineering. Due to the parallel‐processing nature and strong‐learning ability, the neural network has become a promising approach to solving the Moore–Penrose inverse recently. However, almost all the existin...
| Auteurs principaux: | , |
|---|---|
| Format: | Article |
| Langue: | English |
| Publié: |
Hindawi-IET
2022-12-01
|
| Collection: | IET Signal Processing |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1049/sil2.12156 |