expensive, is useful especially in the cases where modelling data is very limited. Moreover, it has been shown that not only is the leave-one-out cross validation generalisation estimate is a better measure, the worst-case error of this estimate is not much worse than that of the training error estimate [8]. In our work, the number of data points we have is limited to 167. We therefore use the leave-one-out cross-validation method to measure the performance of the TS neuro-fuzzy model. 

## Results And Discussions

The classification result is assessed using the leave-one-out cross validation process described above. The performance of the TS neuro-fuzzy model is illustrated are shown in Table 1 below. 

Table 1: The table shows the classification results that are obtained when using the neuro-fuzzy model 

| neuro-fuzzy model     |                     |          |
|-----------------------|---------------------|----------|
| Method                | Misclassified cases | Accuracy |
| Varied threshold      | 14                  | 91.62%   |
| Fixed threshold (0.5) | 16                  | 90.42%   |

From the table we can see that the method of optimising the threshold is slightly superior 

![0_image_0.png](0_image_0.png) in that it allows us to classify two more fault cases. The different thresholds that were selected during the classification are shown in Figure 2 below. Figure 2: An illustration of the different thresholds that were selected for fault positions x, y and z. 

The method used in this paper therefore gives a slightly better accuracy than the Bayesian trained neural network used by Marwala [2]. 

## Conclusion
