
| Table 11: Showing the results obtained for the simulations done for Output 4   |            |
|--------------------------------------------------------------------------------|------------|
| for the ANFIS                                                                  |            |
| Output 4                                                                       | Polynomial |
| Pi Curve                                                                       |            |
| MSE                                                                            | 0.014330   |
| Training Time (s)                                                              | 132        |
| Execute Time (s)                                                               | 0.046      |
| No. of Training Cycles                                                         | 120        |
| No. Fuzzy Rules                                                                | 16         |

The Adaptive Neuro-Fuzzy Inference System was easy to implement and the results obtained show that it can accurately model a system as shown by Output 4. The improvement in the accuracy for Output 4 was significant.

The simulations for the ANFIS produced better accuracy than the SVMs and had similar training time. However, the ANFIS executed much faster than the SVMs. Summing the MSE of each ANFIS to produce the effective error of the 4 ANFIS working as a committee to predict the steam generator outputs, gives an approximate MSE of 0.06858.

![0_image_2.png](0_image_2.png)

![0_image_4.png](0_image_4.png)

Figure 19: Showing the Training and. Validation error vs. the training cycles for the ANFIS using Gaussian Input Membership Functions for Output 1 .

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

![0_image_3.png](0_image_3.png)

