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Figure 1: Semantic Object Diagram for Student's Record
As illustrated, the student record can be split into three objects; personal details, education and activities. Splitting the record into three specialized objects reduces the complexity of student's record. Several specialized networks were then constructed to represent each object in student's record. The specialized networks are PERSONAL_DETAILS, EDUCATION and ACTIVITIES (see Figure 2).
Figure 2: Multi-Network for Student's Object
An integrating network, STUDENT is constructed to integrate the outputs from the specialized network. This is the third stage or final stage in implementing multi backpropagation network. Figure 3 summarizes the processes involved.
Figure 3: The Multi Backpropagation Modeling Process
3.0 Applying Multi Backpropagation
The construction of Multi backpropagation involves several phases as shown in Figure 3. The main modules are normalization, training module, storing weights and application module.
Normalization
Normalization is a process of splitting data into several smaller data sets. Typically, in developing a system or particularly in intelligent system development, the knowledge engineer is responsible to design the system's processes and decides which attributes or parameters that are important and relevant to the system. The decision is based on the expert advice and his understanding on the problem domain. In this study the grouping criteria are based on the questionnaires forms, which were designed by the experts.
Training the backpropagation network is based on the training algorithm as discussed in previous chapter. The general training process is as follow:
Step 1:
Loading:
Load input data
Step 2:
Feeding:
Broadcast the input signals to the next layer
Step 3:
Calculate Error:
Calculate error information
Step 4:
Updating:
Update the weights
Step 5:
Stopping:
Checks stopping criteria
Storing Weights
As a result of the training or the learning process the best weights will be produced. All specialized network and integrating network will have different sets of weights as they are trained individually. The weights are knowledge that represents the networks. The weights of specialized networks are the knowledge that describes the sub-domain or category presented, whereas the weights of integrating network are a predefined knowledge or rules that tell how the network should interpret the combination of specialized networks. Figure 4 shows samples of weights and how the weights are stored.
Figure 4: Storing Weights
Application Module
Application module uses output from the specialized networks as its input and produced the final output. Prior to that, the network itself is trained with all possible specialized network output that randomly generated by the computer. The complete step-by-step processes in multi backpropagation network application and the role of integrating network are as follows:
Step 1:
For each specialized networksLoading:
Load input data.
Load weights files.Testing:
Feed into the network.
Apply the weights.Output:
Store the output.
Step 2:
For integrating networkLoading:
Get the output of all specialized networks as input.
Load weights files.Testing:
Feed into the network.
Apply the weights.Output:
Store the output.
Step 3:
Display report.
Figure 5 shows a complete architecture in training multi backpropagation network. As shown, there are two sections: training and application sections. Training section is where the networks are trained and their weights are stored while, in application section the weights and a new data are used by the networks to produce the output. The outputs of the specialized networks are then fed into the integrated network.
Figure 5: Incorporating Specialized and integrating networks
Submitted: 18/04/2004