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An RBF network with the same number of hidden nodes as the MLP was implemented to observe how long it would take to train and execute in comparison to the MLP. If the networks have similar complexities it was observed that the RBF trained faster than the MLP, and forward executed with a similar speed to the MLP. The RBF network with 8 hidden nodes that was implemented took 3 seconds to train and 0.016 seconds to execute. However, the RBF model with the 8 hidden nodes had a larger MSE than the MLP. 

## D. **Committees**

A simple averaging committee was implemented for both the MLP and RBF using the NETLAB Toolbox. The averaging committee consisted of 10 MLP networks with identical architectures. Each network in the committee was trained using the same training dataset, however, each network was initialised differently and trained independently. The final output was taken as the average of the individual outputs for each network. Also, a committee consisting of 10 RBF networks was constructed the same way as described for the MLP committee. The architectures of each network in the committees used the optimum parameters found using the standard approaches. 

It was found that the averaging committees only marginally improved the accuracy obtained. The committees took longer to train and execute which can only be expected as the committee is effectively 10 networks. Table 2, shows the results captured for the committee implemented. From Equation 5, increasing the number of neural networks in the committees would result in the error reducing as the number of neural networks increased. 

| networks with identical architectures.   MLP Committee   | RBF Committee   |          |
|----------------------------------------------------------|-----------------|----------|
| MSE of Test Dataset                                      | 0.0746639       | 0.076200 |
| Time to Train (s)                                        | 80              | 129      |
| Time to Execute (s)                                      | 0.172           | 0.407    |

Another implementation of a committee that was tested was bagging. In bagging, each network is trained on a bootstrap 