Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 43

It is the simplest form of neural network model containing only one layer of input nodes which receives the weighted input and send it to the subsequent layer of receiving nodes. In some cases, there may be only one neuron exists at the receiving end. Even a single neuron of ANN has astonishing computational capability. As the activity of the input is limited to receiving and passing of input signal and it does not perform any computation, thus only true layer of neuron is single-layer network is the output layer. The basic model of single layer neural network is shown in Figure 2.7. The yellow nodes denote the input layer which receives x₁, x₂, x₃, ……, xN as the input and send it to the output layer represented by green nodes. Each of the nodes in the input layer is connected with each node in the output layer through the connection weight. If the weight value is zero, then indicates no connection between the nodes. The output nodes calculate the output values and generate the output y₁, y₂, y₃, ……, yN. Because of only one layer of link between the input and output, this model is termed as single layer network. It can only learn linear functions. The model shown in Figure 2.7 is fully connected.