Research Article
Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study
Table 9
The consequent parameters of ANFIS FO Sugeno model trained with HL algorithm.
| Rule no. | | | | |
| 1 | –0.3475 | 0.1141 | 0.09476 | 0.4712 | 2 | 0.4908 | 0.01827 | 0.03587 | 0.06537 | 3 | –0.5326 | 0.082 | 0.3158 | 0.3164 | 4 | –0.142 | 0.6265 | 0.1154 | 0.508 | 5 | –0.8807 | 0.3169 | 0.3492 | 0.5938 | 6 | 0.3319 | –0.05292 | 0.1754 | 0.1892 | 7 | –1.996 | –0.05069 | 0.4055 | 1.978 | 8 | –0.3329 | 0.3839 | 0.2737 | 0.5203 | 9 | –1.28 | 1.041 | 0.2528 | 0.2626 | 10 | 0.2578 | 0.07892 | 0.05012 | 0.2143 | 11 | 0.2012 | 0.08151 | 0.1871 | 0.3131 | 12 | 0.2403 | 0.05555 | 0.1689 | 0.1762 | 13 | 0.673 | –1.131 | 0.1542 | 0.7394 | 14 | 0.4164 | 0.01532 | 0.1737 | 0.3085 | 15 | 0.1619 | –0.5922 | 0.2246 | 0.231 | 16 | –1.471 | 5.782 | –0.4443 | –2.232 | 17 | 0.008048 | 0.6415 | –0.0189 | –0.08517 | 18 | –0.4688 | 2.999 | –0.7868 | –0.7847 | 19 | –0.215 | 0.09277 | 0.07536 | 0.367 | 20 | 0.1457 | 0.03197 | 0.06851 | 0.1165 | 21 | –0.3208 | 0.07498 | 0.2976 | 0.2996 | 22 | –0.3354 | 0.3712 | 0.1312 | 0.5879 | 23 | –0.4769 | 0.2513 | 0.3433 | 0.588 | 24 | 0.1199 | 0.1176 | 0.1524 | 0.1659 | 25 | 0.3456 | –0.0816 | 0.07721 | 0.3656 | 26 | –0.1232 | 0.3988 | 0.1172 | 0.1992 | 27 | 0.008071 | 0.06087 | 0.2541 | 0.2582 |
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