IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, January 2010 www.IJCSI.org 

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Fig 9 Initial Network performances at 100 epochs for 15 mm thickness using logsig & tansig as transfer functions respectively.

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 The number of epochs taken to reach saturation upon training was more in case of logsig transfer function when compared with tansig transfer function. The performance level was considerably more in case of logsig when performed for 15 mm thickness of the valve body. 

6.1 Prediction Performance of the Network Trained for 17 mm Dataset The developed ANN network was constituted to training for 17 mm thickness data sets. Prearranged datas for composition and property along with 17 mm constant thickness was given as input. The performance goal for the network was set to zero. As prescribed earlier, the network was trained for tansig and logsig type of transfer functions.. Figures 13 and 14 demonstrate the initial performance of the network model for logsig and tansig mode of transfer function. Likewise figure 15 typify the final Network performances at 1268951epochs for 17 mm thickness using logsig as transfer function and figure 16 represent the final outcomes at 1280176 epochs for 17 mm thickness using tansig as transfer function. As in previous case the network has been fully trained before reaching the goal and further training was stopped. 

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Fig. 12 Final Network performances at 1268951 & 1280176 epochs for 17 mm thickness using logsig & tansig as transfer functions respectively.

The number of epochs taken to reach saturation upon training was almost equal in both the cases of transfer function when performed for 17 mm thickness of the valve body. 

6.1 Prediction Performance of the Network Trained for 19 mm Dataset The network was reinitialized for training for 19 mm thickness data sets. The performance goal for the network was set to zero. As given earlier, the adopted network was trained for tansig and logsig transfer functions. The performance of the developed model for these data sets on initial training could not yield good results. The figures 17 and 18 picture the initial performance of the model at 100 epoch level. Upon further rigorous training of the network model, for being the goal set to zero, remarkable variations in training was observed. Unaltered performance could be observed at the early stage of the training. But training was continued until a maximum epoch of approximately 1100000 was reached. Very minimal down fall of curve, if not improvement in network performance as depicted in figure 19 and 20 was visualized at the time of stopping the network for the specified goal and epochs. The network performance rate was better in case of tansig transfer function in this case. 

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Fig. 13 Initial Network performances at 100 epochs for 19 mm thickness using logsig & tansig as transfer functions respectively.

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