SLAMorris Final Thesis After Corrections.pdf - Cranfield University

Figure 7-5: Bayesian Network for H6 classification
7.9 Neural Network Approach
The Neural Network method was implemented using a feed forward Neural
Network with a single hidden layer. The network consisted of 17 input nodes
which use the same input as the nodes identified in the Bayesian Network. The
hidden layer consisted of 11 nodes, with 6 output nodes representing the
various classifications. A sigmoid transfer function and back propagation were
used for learning. The training set comprised of half of Data Set 1 as described
in Section 7.5. The learning rate was initially set at 0.5 and training was set to
stop when the error rate was 0.01 or 25000 iterations were reached. The
remaining fragments in the corpus were used for testing the network. The
implemented network was designed and written completely for this research;
this enabled the method to be adapted to the specification of this research.
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