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Table 5
Comparative analysis between ANN and ANFIS.
| Sr. No. | Feature | ANN | ANFIS |
|---|---|---|---|
| 1. | Accuracy | 92–95% | 97–99% |
| 2. | Fault classification | Requires extensive training data | Efficient even with moderate training data |
| 3. | Fault detection speed | Moderate due to complex training weights | Faster due to hybrid learning approach |
| 4. | Adaptability | High for nonlinear systems but lacks interpretability | High with rule-based flexibility |
| 5. | Model interpretability | Low (black-box approach) | High (explainable with fuzzy rules) |
| 6. | Training time | Longer for large datasets | Relatively faster |
| 7. | Robustness to noise | Sensitive to noise in training data | Less sensitive due to fuzzy logic handling |
| 8. | Fault location precision | Accurate but slightly less consistent | Higher precision and consistency |
| 9. | Ease of implementation | Easier to implement in neural network frameworks | Requires defining fuzzy rules and membership functions |
| 10. | Generalization | Good with diverse training data | Very good due to hybrid learning |
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