Layered neural network fully connected

Figure 15 The general scheme for a fully connected two-layer neural network with four inputs...

A feedforward neural network brings together several of these little processors in a layered structure (Figure 9). The network in Figure 9 is fully connected, which means that every neuron in one layer is connected to every neuron in the next layer. The first layer actually does no processing it merely distributes the inputs to a hidden layer of neurons. These neurons process the input, and then pass the result of their computation on to the output layer. If there is a second hidden layer, the process is repeated until the output layer is reached. 

A multilayer perceptron (MLP) is a feed-forward artificial neural network model that maps sets of input data onto a set of suitable outputs (Patterson 1998). A MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP employs a supervised learning techruque called backpropagation for training the network. MLP is a modification of the standard linear perceptron and can differentiate data that are not linearly separable. 

Artificial neural networks consist of different types of layers. There is the input-layer, one or more hidden layers and an output layer. All these layers can consist of one or more neurons. A neuron in a particular layer is connected to all neurons in the next layer, which is why this is called a feed-forward network. In other networks the neurons might be connected otherwise. An example of a different network is a recurrent neural network where there are also links that connect neurons to other neurons in a previous layer. A fully connected network is a network in which all the neurons from one layer are connected to all neurons in the next layer. 

Figure 2 Schematic diagram of a three-layer, fully-connected, feed-forward computational neural network...

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