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2.3.4 Single-Layer Network

By connecting multiple neurons, the true computing power of the neural networks comes, though even a single neuron can perform substantial level of computation [Ler91]. The most common structure of connecting neurons into a network is by layers. The simplest form of layered network is shown in figure 2.7. The shaded nodes on the left are in the so-called input layer. The input layer neurons are to only pass and distribute the inputs and perform no computation. Thus, the only true layer of neurons is the one on the right. Each of the inputs $ x_1, x_2, x_3, ... , x_N$ is connected to every artificial neuron in the output layer through the connection weight. Since every value of outputs $ y_1, y_2, y_3, ... , y_N$ is calculated from the same set of input values, each output is varied based on the connection weights. Although the presented network is fully connected, the true biological neural network may not have all possible connections - the weight value of zero can be represented as ``no connection".

Figure 2.7: Single Layer Neural Network
\begin{figure}
\centerline {\epsfysize=2.0in \epsfbox{./figures/figSingleLayer.epsi}}\end{figure}


next up previous
Next: 2.3.5 Multilayer Network Up: 2.3 Artificial Neural Networks Previous: 2.3.3 Artificial Neuron with
Kiyoshi Kawaguchi
2000-06-17