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Next: 2.3.6 Learning Processes Up: 2.3 Artificial Neural Networks Previous: 2.3.4 Single-Layer Network

2.3.5 Multilayer Network

To achieve higher level of computational capabilities, a more complex structure of neural network is required. Figure 2.8 shows the multilayer neural network which distinguishes itself from the single-layer network by having one or more hidden layers. In this multilayer structure, the input nodes pass the information to the units in the first hidden layer, then the outputs from the first hidden layer are passed to the next layer, and so on.

Figure 2.8: Multiple Layer Neural Network
\centerline {\epsfysize=2.0in \epsfbox{./figures/figMultiLayer.epsi}}\end{figure}

Multilayer network can be also viewed as cascading of groups of single-layer networks. The level of complexity in computing can be seen by the fact that many single-layer networks are combined into this multilayer network. The designer of an artificial neural network should consider how many hidden layers are required, depending on complexity in desired computation.

Kiyoshi Kawaguchi