Research Article
A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition
Table 17
Table of comparison of optimized results (ORL database).
| | Method | Images for training | Recognition rate | | Best (%) | Average (%) | Worst (%) |
| Mendoza et al. [4]
(FIS) | 8 | 97.50% | 94.69% | 91.50% | Sánchez et al. [38]
(FA) | 8 | 100% | 100% | 100% | Sánchez et al. [39]
(MGNNs + complexity) | 8 | 100% | 99.27% | 98.61% | | Proposed method | 8 | 100% | 100% | 100% | Azami et al. [43]
(CGA + PCA) | 5 | 96.5% | 95.91% | 95.37% | Ch’Ng et al. [3]
(PCA + LDA) | 5 | 96.5% | 94.75% | 94% | Sánchez et al. [38]
(FA) | 5 | 99% | 98.30% | 98% | Sánchez et al. [39]
(MGNNs + complexity) | 5 | 98.43% | 97.59% | 94.55% | | Proposed method | 5 | 99% | 98.5% | 98% |
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