The Structure of an Artificial Neural Network

More than 50 different types of neural network exist. Certain networks are more efficient in optimization others perform better in data modeling and so forth. According to Basheer (2000) the most popular neural networks today are the Hopfield networks, the Adaptive Resonance Theory (ART) networks, the Kohonen networks, the counter propagation networks, the Radial Basis Function (RBF) networks, the backpropagation networks and recurrent networks. 

To explain the basics of the structure of a neural network it is best to choose the simplest form of a neural network, the feed-forward neural network. In Fig. 27.1 a feed-forward network is shown. 

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. 

Process Dynamics and Control Modeling for Control and Prediction. Brian Roffel and Ben Betlem. 2006 John Wiley Sons Ltd. 

To explain how the network calculates the output, first the smallest part of the netwoik will be observed closer the neurom 

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