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 All the Artificial Intelligence Methods investigated and simulated were capable of generalising on unseen data reasonably well. This section will discuss the principle findings of the investigations and simulations carried out. 

The neural networks were difficult to tune as they had the most parameters to adjust. Therefore, finding the optimum parameters to model the given steam generator dataset was a tedious task. Also, the results were not easily reproducible due to the optimisation algorithm used. Therefore, many networks had to be trained in order to achieve accurate results and the best network found was retained. 

It was found that the MLP and RBF with similar accuracy had significantly different complexities. In order, to obtain an RBF with a comparable accuracy to that of the MLP produced, the number of hidden nodes in the RBF had to be increased to over 3 times the number of that of the MLP. While the RBF is supposed to be faster during the training process [2], the increased complexity of the RBF network resulted in a significantly increased training time. 

The averaging committees of neural networks only slightly increased the accuracy obtained. Since the number of neural networks being trained and executed was more than one, the training and execution time increased compared to the time obtained for an individual neural network. The committee constructed using bagging improved the accuracy slightly from that of the simple averaging committee, however, it took slightly longer to train as the bootstrap dataset had to created. 

Using the Bayesian techniques for the MLP improved the accuracy obtained by the MLP. However, it increased the training and execution time required by the neural network produced. This result should be expected as the Bayesian technique is similar to a committee of 100 networks since 100 samples were retained. 

The SVM was more accurate than the neural networks. 

However, a separate SVM had to be implemented for each output. Also, the SVMs had less parameters to tune, thus, making them much easier to implement. The SVM took a long time to train in comparison to the neural networks even though the neural networks were modelling all four relationships between the inputs and outputs at once. Also, the SVMs were slower to execute on unseen data. The SVMs produced were of a comparable accuracy to the Bayesian MLP. Yet, the Bayesian MLP took at least twice the amount of time to train, while the Bayesian MLP was faster to execute. The results obtained from the SVMs were easily reproduced. 

The ANFIS out performed all the other methods. The training time for the ANFIS was more than the neural networks but still less than the other methods. However, it should be noted that the training time was highly dependent on the input membership function chosen. Therefore, the membership function with the best accuracy and least training time was selected for each ANFIS implemented. The execution time for the ANFIS was fast and comparable to the execution time obtained for the neural networks. 