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5 Particle Identification with a Neural Network
5.2 Artificial Neural Networks
The term artificial neural network (NN) describes where they come from: the task to replicate
the main feature of the human brain, i.e. the network of neurons. The hope was that a combination
of the complex design of the brain and the technology of modern micro processors
could combine the advantages of both: good pattern recognition capabilities and fast signal
transmission.
An artificial neural network can be thought of as a sort of ‘black box’ processing system; its
operational capabilities allow the reproduction of an application between two sets of
vectors. In a neural network the basic units are called neurons. A neuron has several input and
output connections, and the weighted sum of all the signals received by a neuron generates its
activation
Ø
ÛÜ
Ü are the input values, Û are the weights and is a threshold potential (see 5.4). The activation
function is very often chosen as a sigmoidal function such as
Ü
The constant Ì sets the gain of the activation function. The output Ó, the signal which is sent
to the neighbouring cells, is calculated from the activation by an output function
Ó ÓÙØ
ÜÌ
In the simplest case, the output function is the identity function, so that
Ó ÓÙØ
With this simple neuron, various types of networks and architectures can be built. One of the
most widely used is the feed-forward, multilayer neural network with supervised training. In
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x 2
x 1
w 2
w 1
Figure 5.4: A simple neural network consisting
of two input neurons, Ü and Ü ,
with specific weights Û and Û , and one
output neuron Ý with a threshold .
y
Figure 5.5: Schematic of a three layer feedforward
neural network.